From cd23d6f66f4dacafc6db4743da9db8a6c5c85dee Mon Sep 17 00:00:00 2001 From: vly12 Date: Fri, 15 Mar 2024 10:24:05 +0000 Subject: [PATCH 01/15] Had to input eval = FALSE into the second setup codechunk to bypass it. Pandas Module was not found. --- intro.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/intro.Rmd b/intro.Rmd index 753f7c0..5b9de4a 100644 --- a/intro.Rmd +++ b/intro.Rmd @@ -27,7 +27,7 @@ tutorial_options( ) ``` -```{python pandas-setup, include = FALSE} +```{python pandas-setup, include = FALSE, eval = FALSE} # Import pandas library import pandas as pd From 1ef6844ce193f18ef86cb416089417bfe7c40bbd Mon Sep 17 00:00:00 2001 From: vly12 Date: Fri, 15 Mar 2024 10:27:34 +0000 Subject: [PATCH 02/15] Changed some of the .png to .PNG in order to correctly map to the image files. --- intro.Rmd | 14 ++++++------- intro.html | 58 +++++++++++++++++++++++++++--------------------------- 2 files changed, 36 insertions(+), 36 deletions(-) diff --git a/intro.Rmd b/intro.Rmd index 5b9de4a..4abb271 100644 --- a/intro.Rmd +++ b/intro.Rmd @@ -87,7 +87,7 @@ You will see the interface looks like this. There are three main tabs in Jupyter - **Clusters** For using IPython in parallel with your cluster (beyond the scope of this training guidance) ```{r startsession, fig.align='center', out.width="100%"} -knitr::include_graphics("images/python-pwb-startsession.png") +knitr::include_graphics("images/python-pwb-startsession.PNG") ```
@@ -99,7 +99,7 @@ knitr::include_graphics("images/python-pwb-newnb.png") ``` ```{r blanknb, fig.align='center', out.width="100%"} -knitr::include_graphics("images/python-pwb-blanknb.png") +knitr::include_graphics("images/python-pwb-blanknb.PNG") ```
@@ -117,7 +117,7 @@ Jupyter notebook is a modal editor which means that the keyboard does different - **Edit mode** - it is indicated by a green cell border and left sidebar, and a prompt showing in the editor area: ```{r editmode, fig.align='center', out.width="100%"} -knitr::include_graphics("images/edit-mode.png") +knitr::include_graphics("images/edit-mode.PNG") ``` When a cell is in edit mode, you can type things such as Python codes into the cell, like a normal text editor. Enter edit mode by pressing Enter or using the mouse to click on a cell’s editor area. @@ -125,7 +125,7 @@ When a cell is in edit mode, you can type things such as Python codes into the c - **Command mode** - Once you click somewhere else outside the cell or press “esc” on keyboard, the cell turns into Command mode. Command mode is indicated by a grey cell border and a blue sidebar: ```{r commandmode, fig.align='center', out.width="100%"} -knitr::include_graphics("images/command-mode.png") +knitr::include_graphics("images/command-mode.PNG") ``` When you are in command mode, you are able to edit the notebook as a whole, but not type into individual cells. Most importantly, in command mode, the keyboard is mapped to a set of shortcuts that let you perform notebook and cell actions efficiently. For example, if you are in command mode and you press C and V, you will copy and paste the current cell. @@ -137,7 +137,7 @@ A full list of useful shortcuts is available by going to "Help > Keyboard Shortc Markdown text cells support plain text, Markdown and HTML. It will be useful to create headings, text instructions etc using markdown to organise the notebook like a written document. A cell can be changed from code mode to markdown mode by going to the top menu bar "Cell > Cell Type > Markdown". Or select “Markdown” from the dropdown list: ```{r markdown, fig.align='center', out.width="100%"} -knitr::include_graphics("images/markdown-menu.png") +knitr::include_graphics("images/markdown-menu.PNG") ``` Or press M while in Command Mode and highlighting the cell. @@ -145,13 +145,13 @@ Or press M while in Command Mode and highlighting the cell. Here is an example of typing some text in a markdown cell. You can use hash key “#” to indicate the size of heading, followed by a space and the text. ```{r markdown-example1, fig.align='center', out.width="100%"} -knitr::include_graphics("images/markdown-example1.png") +knitr::include_graphics("images/markdown-example1.PNG") ``` Then press *Shift + Enter* to finish. ```{r markdown-example2, fig.align='center', out.width="100%"} -knitr::include_graphics("images/markdown-example2.png") +knitr::include_graphics("images/markdown-example2.PNG") ``` #### Python Library diff --git a/intro.html b/intro.html index 0d571bd..5482ea7 100644 --- a/intro.html +++ b/intro.html @@ -12,7 +12,7 @@ - + Intro to Python @@ -182,14 +182,14 @@

Open a New Jupyter Notebook

  • Clusters For using IPython in parallel with your cluster (beyond the scope of this training guidance)
    • -

    +


    To open a new Jupyter Notebook, click the “New” drop down menu on the Files tab and select “Python 3.10.2” under the notebooks heading. This will open a blank Notebook with an IPython console running underneath it.

    -

    +


    The IPython console is used to input and execute Python code interactively. Outputs, errors and warning messages are directly shown @@ -214,7 +214,7 @@

    Command and Edit Modes

  • Edit mode - it is indicated by a green cell border and left sidebar, and a prompt showing in the editor area:
    • -

    +

    When a cell is in edit mode, you can type things such as Python codes into the cell, like a normal text editor. Enter edit mode by pressing Enter or using the mouse to click on a cell’s editor area.

    @@ -224,7 +224,7 @@

    Command and Edit Modes

    mode. Command mode is indicated by a grey cell border and a blue sidebar: -

    +

    When you are in command mode, you are able to edit the notebook as a whole, but not type into individual cells. Most importantly, in command mode, the keyboard is mapped to a set of shortcuts that let you perform @@ -243,14 +243,14 @@

    Markdown Text Cells

    from code mode to markdown mode by going to the top menu bar “Cell > Cell Type > Markdown”. Or select “Markdown” from the dropdown list:

    -

    +

    Or press M while in Command Mode and highlighting the cell.

    Here is an example of typing some text in a markdown cell. You can use hash key “#” to indicate the size of heading, followed by a space and the text.

    -

    +

    Then press Shift + Enter to finish.

    -

    +

    Python Library

    @@ -847,11 +847,11 @@

    Feedback

    ")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", - engine = "\"python\""), engine = "python"), list( - label = "csv-read-columns1", code = "# Read in the dataset with specific columns\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) \n\n# Check the first few rows of the dataset. Default is 5 rows. \nborders2.head() ", - opts = list(label = "\"csv-read-columns1\"", exercise = "TRUE", - exercise.setup = "\"pandas-setup\"", engine = "\"python\""), - engine = "python")), code_check = NULL, error_check = NULL, + eval = "FALSE", engine = "\"python\""), engine = "python"), + list(label = "csv-read-columns1", code = "# Read in the dataset with specific columns\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) \n\n# Check the first few rows of the dataset. Default is 5 rows. \nborders2.head() ", + opts = list(label = "\"csv-read-columns1\"", exercise = "TRUE", + exercise.setup = "\"pandas-setup\"", engine = "\"python\""), + engine = "python")), code_check = NULL, error_check = NULL, check = NULL, solution = NULL, tests = NULL, options = list( eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA, @@ -900,11 +900,11 @@

    Feedback

    ")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", - engine = "\"python\""), engine = "python"), list( - label = "csv-read-columns2", code = "# Rearrange the columns\nborders3 = borders2[['URI', 'Specialty', 'HospitalCode']]\n\n# Check the first few rows of the dataset. Default is 5 rows. \nborders3.head()", - opts = list(label = "\"csv-read-columns2\"", exercise = "TRUE", - exercise.setup = "\"pandas-setup\"", engine = "\"python\""), - engine = "python")), code_check = NULL, error_check = NULL, + eval = "FALSE", engine = "\"python\""), engine = "python"), + list(label = "csv-read-columns2", code = "# Rearrange the columns\nborders3 = borders2[['URI', 'Specialty', 'HospitalCode']]\n\n# Check the first few rows of the dataset. Default is 5 rows. \nborders3.head()", + opts = list(label = "\"csv-read-columns2\"", exercise = "TRUE", + exercise.setup = "\"pandas-setup\"", engine = "\"python\""), + engine = "python")), code_check = NULL, error_check = NULL, check = NULL, solution = NULL, tests = NULL, options = list( eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA, @@ -952,11 +952,11 @@

    Feedback

    ")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", - engine = "\"python\""), engine = "python"), list( - label = "delete-column", code = "# Delete URI column\ndel borders2[\"URI\"]\n\n# Check the first few rows of the dataset. Default is 5 rows. \nborders2.head()", - opts = list(label = "\"delete-column\"", exercise = "TRUE", - exercise.setup = "\"pandas-setup\"", engine = "\"python\""), - engine = "python")), code_check = NULL, error_check = NULL, + eval = "FALSE", engine = "\"python\""), engine = "python"), + list(label = "delete-column", code = "# Delete URI column\ndel borders2[\"URI\"]\n\n# Check the first few rows of the dataset. Default is 5 rows. \nborders2.head()", + opts = list(label = "\"delete-column\"", exercise = "TRUE", + exercise.setup = "\"pandas-setup\"", engine = "\"python\""), + engine = "python")), code_check = NULL, error_check = NULL, check = NULL, solution = NULL, tests = NULL, options = list( eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA, @@ -987,16 +987,16 @@

    Feedback

    @@ -1060,12 +1060,12 @@

    Feedback

    From fdfd9d8c4dae81b6e1b040fdf658e89714d0b715 Mon Sep 17 00:00:00 2001 From: vly12 Date: Fri, 15 Mar 2024 17:29:32 +0000 Subject: [PATCH 03/15] adding code for mean, median, description (summary), and frequency/crosstab/margin --- intro.Rmd | 108 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) diff --git a/intro.Rmd b/intro.Rmd index 4abb271..57bfc30 100644 --- a/intro.Rmd +++ b/intro.Rmd @@ -347,6 +347,114 @@ hello_world <- "Hello World" print(hello_world) ``` +## Explore + +### Mean/Median & Summary + +* `mean()` and `median()` are passed arrays of values (usually from a data frame) to return the mean and median value. + +* `describe()` returns all summary statistics based on a given array. + +In the exercise below, you have the borders data-set loaded as `borders_data`. See if you can get the mean value for `LengthOfStay`, store it in a variable, and print that variable. Use the hint button if you need some help. + +```{python mean, exercise=TRUE, exercise.eval=TRUE} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data +``` + +```{python mean-hint-1} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data["LengthOfStay"] +``` + + +```{python mean-hint-2} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data["LengthOfStay"].mean() +``` + + +```{python mean-hint-3} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +mean_value = borders_data["LengthOfStay"].mean() + +print(mean_value) +``` + +```{python mean-solution} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +mean_value = borders_data["LengthOfStay"].mean() + +print(mean_value) +``` + +```{python mean-check} +grade_code() +``` + + +### Frequencies & Crosstabs + +* Frequency: `["").value_counts()` +* Crosstab: `pd.crosstab([""], [""])` +* Crosstab & Add Col/Row Totals: `pd.crosstab([""], [""], margins = True)` +tab +Create a crosstab for `HospitalCode` and `Sex`, add column and row totals, store the table in a variable, and print it out. Use the hint button if you need some help. + +```{python freq, exercise=TRUE} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python freq-hint-1} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +...pd.crosstab(...) +``` + +```{python freq-hint-2} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"]...) +``` + +```{python freq-hint-3} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"], margins = True) +print(crosstab_hospitalcode_sex) +``` + +```{python freq-solution} +import pandas as pd +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"], margins = True) + +print(crosstab_hospitalcode_sex) +``` + +```{python freq-check} +grade_code() +``` ## Help & Feedback From f1abbd4489ad4cb539126e215062d3602a1db2ba Mon Sep 17 00:00:00 2001 From: vly12 Date: Wed, 20 Mar 2024 17:53:32 +0000 Subject: [PATCH 04/15] Removed import pandas text. Started Location Section --- intro.Rmd | 264 +++++++++++++++++++++++++++++++++++++++--- python-training.Rproj | 0 2 files changed, 251 insertions(+), 13 deletions(-) mode change 100644 => 100755 intro.Rmd mode change 100644 => 100755 python-training.Rproj diff --git a/intro.Rmd b/intro.Rmd old mode 100644 new mode 100755 index 57bfc30..95319d9 --- a/intro.Rmd +++ b/intro.Rmd @@ -8,6 +8,7 @@ output: runtime: shiny_prerendered --- + ```{r setup, include=FALSE} # Author: Tina Fu # Original Date: February 2024 @@ -357,15 +358,15 @@ print(hello_world) In the exercise below, you have the borders data-set loaded as `borders_data`. See if you can get the mean value for `LengthOfStay`, store it in a variable, and print that variable. Use the hint button if you need some help. -```{python mean, exercise=TRUE, exercise.eval=TRUE} -import pandas as pd +```{python mean, exercise = TRUE, exercise.setup = "pandas-setup"} + borders_data = pd.read_csv("data/borders_inc_age.csv") borders_data ``` ```{python mean-hint-1} -import pandas as pd + borders_data = pd.read_csv("data/borders_inc_age.csv") borders_data["LengthOfStay"] @@ -373,7 +374,7 @@ borders_data["LengthOfStay"] ```{python mean-hint-2} -import pandas as pd + borders_data = pd.read_csv("data/borders_inc_age.csv") borders_data["LengthOfStay"].mean() @@ -381,7 +382,7 @@ borders_data["LengthOfStay"].mean() ```{python mean-hint-3} -import pandas as pd + borders_data = pd.read_csv("data/borders_inc_age.csv") mean_value = borders_data["LengthOfStay"].mean() @@ -390,7 +391,7 @@ print(mean_value) ``` ```{python mean-solution} -import pandas as pd + borders_data = pd.read_csv("data/borders_inc_age.csv") mean_value = borders_data["LengthOfStay"].mean() @@ -405,28 +406,28 @@ grade_code() ### Frequencies & Crosstabs -* Frequency: `["").value_counts()` +* Frequency: `["").value_counts()]` * Crosstab: `pd.crosstab([""], [""])` * Crosstab & Add Col/Row Totals: `pd.crosstab([""], [""], margins = True)` tab Create a crosstab for `HospitalCode` and `Sex`, add column and row totals, store the table in a variable, and print it out. Use the hint button if you need some help. -```{python freq, exercise=TRUE} -import pandas as pd +```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} + borders_data = pd.read_csv("data/borders_inc_age.csv") ``` ```{python freq-hint-1} -import pandas as pd + borders_data = pd.read_csv("data/borders_inc_age.csv") ...pd.crosstab(...) ``` ```{python freq-hint-2} -import pandas as pd + borders_data = pd.read_csv("data/borders_inc_age.csv") crosstab_hospitalcode_sex = pd.crosstab( @@ -434,7 +435,7 @@ crosstab_hospitalcode_sex = pd.crosstab( ``` ```{python freq-hint-3} -import pandas as pd + borders_data = pd.read_csv("data/borders_inc_age.csv") crosstab_hospitalcode_sex = pd.crosstab( @@ -443,7 +444,187 @@ print(crosstab_hospitalcode_sex) ``` ```{python freq-solution} -import pandas as pd + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"], margins = True) + +print(crosstab_hospitalcode_sex) +``` + +```{python freq-check} +grade_code() +``` + +## Wrangle – Part 1 + +For the next sections, we will focus on using the pandas module manipulate data and data frames. + + +### Location + +There are multiple ways to wrangling data in Python - some are more straightforward than others. + +To begin efficiently locating and manipulating data, it would be beneficial to become familiar with the less straightforward concept of locating the data. + +The `loc` and `iloc` attributes are used indexing, which allows for the locating of data from a dataframe based on row and column labels. + +`loc` - Label-based indexing (such as Hospital Codes, Names, etc.). +`iloc` - Index-based indexing (remember Python indexes count from 0 onwards). + +By default, `loc` and `iloc` locates row indexes, instead of column. + + +`.loc['']` will select all the rows with the row_label. +`.iloc['']` will select the row at index == row_index_label. + +However, you can use loc and iloc to locate rows and columns alike: + +`.loc['']` will select all the rows with the row_label. +`.iloc['']` will select the row at index == row_index_label. + +### Select / Slice Out Columns + +By far, the most straightfoward way to select a column is by the following method: + +* Selecting/Slicing Out a column: `[""]` + +However, to be able to start retrieve and manipulate data well, it would be useful to become familiar with loc and iloc. + +* Selecting/Slicing Out a column: `.loc[""]` + +Using the data stored in the borders_inc_age csv file, read it in, select/slice out the `HospitalCode` column, and print out the selected column. + +```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +column_hospital_code = + +``` + +```{python freq-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +column_hospital_code = borders_data +``` + +```{python freq-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +column_hospital_code = borders_data[""] +``` + +```{python freq-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +column_hospital_code = borders_data["HospitalCode"] + +print(column_hospital_code) +``` + +```{python freq-solution} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +column_hospital_code = borders_data["HospitalCode"] + +print(column_hospital_code) +``` + +```{python freq-check} +grade_code() +``` + + +### Select Rows + + +```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python freq-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +...pd.crosstab(...) +``` + +```{python freq-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"]...) +``` + +```{python freq-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"], margins = True) +print(crosstab_hospitalcode_sex) +``` + +```{python freq-solution} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"], margins = True) + +print(crosstab_hospitalcode_sex) +``` + +```{python freq-check} +grade_code() +``` + +### Add a New Row/Column + + +```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python freq-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +...pd.crosstab(...) +``` + +```{python freq-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"]...) +``` + +```{python freq-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"], margins = True) +print(crosstab_hospitalcode_sex) +``` + +```{python freq-solution} + borders_data = pd.read_csv("data/borders_inc_age.csv") crosstab_hospitalcode_sex = pd.crosstab( @@ -456,6 +637,63 @@ print(crosstab_hospitalcode_sex) grade_code() ``` +### Delete a Row/Columns + + +```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python freq-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +...pd.crosstab(...) +``` + +```{python freq-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"]...) +``` + +```{python freq-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"], margins = True) +print(crosstab_hospitalcode_sex) +``` + +```{python freq-solution} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +crosstab_hospitalcode_sex = pd.crosstab( + borders_data["HospitalCode"], borders_data["Sex"], margins = True) + +print(crosstab_hospitalcode_sex) +``` + +```{python freq-check} +grade_code() +``` + + +##Wrangle – Part 2 + +· Manipulate Strings + +· Aggregate Dataframe + +· Merge Dataframe + ## Help & Feedback #### Feedback diff --git a/python-training.Rproj b/python-training.Rproj old mode 100644 new mode 100755 From 5bda74bd758e1f1751769e274cfbff92f39d7f23 Mon Sep 17 00:00:00 2001 From: vly12 Date: Thu, 21 Mar 2024 15:48:23 +0000 Subject: [PATCH 05/15] Wrangle #1 --- intro.Rmd | 197 +++++++++----- intro.html | 752 ++++++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 872 insertions(+), 77 deletions(-) diff --git a/intro.Rmd b/intro.Rmd index 95319d9..216183f 100755 --- a/intro.Rmd +++ b/intro.Rmd @@ -464,39 +464,47 @@ For the next sections, we will focus on using the pandas module manipulate data ### Location -There are multiple ways to wrangling data in Python - some are more straightforward than others. +There are multiple ways to wrangle data in Python - some are more straightforward than others. -To begin efficiently locating and manipulating data, it would be beneficial to become familiar with the less straightforward concept of locating the data. +To begin efficiently locating and manipulating data, it would be beneficial to become familiar with the concept of locating data in a dataframe. -The `loc` and `iloc` attributes are used indexing, which allows for the locating of data from a dataframe based on row and column labels. +The `loc` and `iloc` attributes are used for indexing, which allows for the locating of data from a dataframe based on row and column labels/indexes. `loc` - Label-based indexing (such as Hospital Codes, Names, etc.). -`iloc` - Index-based indexing (remember Python indexes count from 0 onwards). +`iloc` - Integer-based indexing (remember Python indexes count from 0). -By default, `loc` and `iloc` locates row indexes, instead of column. +By default, `loc` and `iloc` locates row indexes, instead of column indexes. - -`.loc['']` will select all the rows with the row_label. +`.loc['']` will select the first row with row_label as the label index. `.iloc['']` will select the row at index == row_index_label. -However, you can use loc and iloc to locate rows and columns alike: -`.loc['']` will select all the rows with the row_label. -`.iloc['']` will select the row at index == row_index_label. +This does not mean the location attributes are isolated to only locating rows - in fact, the real power of the location attributes is in its ability to freely locate relevant rows, columns, values, etc. + +In the next few sections, we will see how useful the location attributes can be. + ### Select / Slice Out Columns -By far, the most straightfoward way to select a column is by the following method: +By far, the most straightforward way to select a column is by the following method: * Selecting/Slicing Out a column: `[""]` -However, to be able to start retrieve and manipulate data well, it would be useful to become familiar with loc and iloc. +However, by using `loc` and `iloc`, we can start to specify further: -* Selecting/Slicing Out a column: `.loc[""]` +* Selecting/Slicing Out a column: `.loc["",""]` -Using the data stored in the borders_inc_age csv file, read it in, select/slice out the `HospitalCode` column, and print out the selected column. +* Example: `borders_data.loc[:,"Specialty"]` will obtain all the rows from the `Specialty` column. -```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} +You may be wondering why using the `loc` attribute is helpful here when the first method is much more simple. + +Using `loc`, you have the ability to select specific rows within that column + +* Example: `borders_data.loc[0:2,"HospitalCode"]` will obtain the first 3 rows from the `HospitalCode` column. + +Read in the data stored in the borders_inc_age csv file, select/slice out the entire `HospitalCode` column using pandas, and print out the selected column. + +```{python select, exercise = TRUE, exercise.setup = "pandas-setup"} borders_data = pd.read_csv("data/borders_inc_age.csv") @@ -504,184 +512,229 @@ column_hospital_code = ``` -```{python freq-hint-1} +```{python select-hint-1} borders_data = pd.read_csv("data/borders_inc_age.csv") -column_hospital_code = borders_data +column_hospital_code = borders_data.loc ``` -```{python freq-hint-2} +```{python select-hint-2} borders_data = pd.read_csv("data/borders_inc_age.csv") -column_hospital_code = borders_data[""] +column_hospital_code = borders_data.loc[:,] ``` -```{python freq-hint-3} +```{python select-hint-3} borders_data = pd.read_csv("data/borders_inc_age.csv") -column_hospital_code = borders_data["HospitalCode"] +column_hospital_code = borders_data.loc[:,'HospitalCode'] print(column_hospital_code) ``` -```{python freq-solution} +```{python select-solution} borders_data = pd.read_csv("data/borders_inc_age.csv") -column_hospital_code = borders_data["HospitalCode"] +column_hospital_code = borders_data.loc[:,'HospitalCode'] print(column_hospital_code) ``` -```{python freq-check} +```{python select-check} grade_code() ``` ### Select Rows +The pandas library has multiple way to select rows, depending on your criteria. -```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} +* Select Rows by index: `.iloc[]']` +* Example: `borders_data.iloc[0]` *This will show the first row of the dataframe * + +* Select Rows which have a specific value in a specific column: `[[''] == '']` +* Example: `borders_data[borders_data['HospitalCode'] == 'B120H']` *This will show all the rows which have `B120H` in the `HospitalCode` column * + +* Select Rows by index: `.loc['']']` +* Example: `borders_data.loc['row_label']` *This will locate the first instance of the row with row_label as the label index - please note, this csv file does not have row labels* + +Read in the borders_inc_age csv file, and select all the rows with `Specialty == A1`. + +```{python select-row, exercise = TRUE, exercise.setup = "pandas-setup"} borders_data = pd.read_csv("data/borders_inc_age.csv") ``` -```{python freq-hint-1} +```{python select-row-hint-1} borders_data = pd.read_csv("data/borders_inc_age.csv") -...pd.crosstab(...) +rows_of_a1 = borders_data[...] ``` -```{python freq-hint-2} +```{python select-row-hint-2} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"]...) +rows_of_a1 = borders_data[borders_data['...']...] ``` -```{python freq-hint-3} +```{python select-row-hint-3} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"], margins = True) -print(crosstab_hospitalcode_sex) +rows_of_a1 = borders_data[borders_data['Specialty']=='A1'] ``` -```{python freq-solution} +```{python select-row-solution} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"], margins = True) - -print(crosstab_hospitalcode_sex) +rows_of_a1 = borders_data[borders_data['Specialty']=='A1'] ``` -```{python freq-check} +```{python select-row-check} grade_code() ``` ### Add a New Row/Column +Adding a new row/column is straightforward. -```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} +When adding a new row, you can first create a dictionary, where `keys` (column names) and `values` (corresponding values in the new row) are mapped together. + +* Create dictionary of mapped keys (columns) and values (row values): +`new_row_data = {'':'','':'','':''}` + +Then, you can append the new row into the dataframe. + +` = .append(new_row_data)` + +If you want to leave the value blank, you can input `None` into the row_value. + + +* Add new column: `[''] = ` + +This will create a new column with all the rows populated by the row_value. + + +Read in the borders_inc_age file. Create a new column called `number_of_eyebrows` with the row_value as `2` for all the rows. + +```{python new-row, exercise = TRUE, exercise.setup = "pandas-setup"} borders_data = pd.read_csv("data/borders_inc_age.csv") + ``` -```{python freq-hint-1} +```{python new-row-hint-1} borders_data = pd.read_csv("data/borders_inc_age.csv") -...pd.crosstab(...) +borders_data[...] ... ``` -```{python freq-hint-2} +```{python new-row-hint-2} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"]...) +borders_data['number_of_eyebrows'] = .... ``` -```{python freq-hint-3} +```{python new-row-hint-3} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"], margins = True) -print(crosstab_hospitalcode_sex) +borders_data['number_of_eyebrows'] = 2 ``` -```{python freq-solution} +```{python new-row-solution} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"], margins = True) - -print(crosstab_hospitalcode_sex) +borders_data['number_of_eyebrows'] = 2 ``` -```{python freq-check} +```{python new-row-check} grade_code() ``` ### Delete a Row/Columns +There are multiple ways to delete rows and columns. -```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} +When deleting a row, you could create a dataframe with the rows containing certain row_values filtered out: + +* Deleting a row: ` = [[''] != '']` + +You can also delete rows based on their index and row_label by slicing the locations. + +* Removing the first row: ` = .iloc[1:]` + +If we want to remove only the 2nd row using the same method, you would need to using locate the specific rows you want to keep, and append them together. + +* Removing the second row: ` = .iloc[:1].append(.iloc[2:]` + +Alternatively, you could use the `drop()` method. + +* Removing the second row: ` = .drop(.index[1])` + +* Removing the second row row_label: ` = .drop('', axis=0)` **`axis=0` specifies you are removing a row.** + +* Removing the second row by index position: ` = .drop(index_position, axis=0)` + +The `drop()` method can also be used to drop columns. + +* Removing a column by label: ` = .drop('', axis=1)` **`axis=1` specifies you are removing a column.** + +* Removing a column by index position: ` = .drop(.columns[index_position], axis=1)` + + +Read in the borders_inc_age csv. Remove all the rows which have `Male` as the value in the `Sex` column. + +```{python delete-row, exercise = TRUE, exercise.setup = "pandas-setup"} borders_data = pd.read_csv("data/borders_inc_age.csv") ``` -```{python freq-hint-1} +```{python delete-row-hint-1} borders_data = pd.read_csv("data/borders_inc_age.csv") -...pd.crosstab(...) +borders_data = ... ``` -```{python freq-hint-2} +```{python delete-row-hint-2} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"]...) +borders_data = borders_data[...] ``` -```{python freq-hint-3} +```{python delete-row-hint-3} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"], margins = True) -print(crosstab_hospitalcode_sex) +borders_data = borders_data[borders_data[...] != ...] ``` -```{python freq-solution} +```{python delete-row-solution} borders_data = pd.read_csv("data/borders_inc_age.csv") -crosstab_hospitalcode_sex = pd.crosstab( - borders_data["HospitalCode"], borders_data["Sex"], margins = True) - -print(crosstab_hospitalcode_sex) +borders_data = borders_data[borders_data['Sex'] != 'Male'] ``` -```{python freq-check} +```{python delete-row-check} grade_code() ``` diff --git a/intro.html b/intro.html index 5482ea7..db3af2c 100644 --- a/intro.html +++ b/intro.html @@ -497,6 +497,419 @@

    Code Exercise

    +
    +

    Explore

    +
    +

    Mean/Median & Summary

    +
      +

    • mean() and median() are passed arrays +of values (usually from a data frame) to return the mean and median +value.

      • +

    • describe() returns all summary statistics based on a +given array.

      • +
    +

    In the exercise below, you have the borders data-set loaded as +borders_data. See if you can get the mean value for +LengthOfStay, store it in a variable, and print that +variable. Use the hint button if you need some help.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data["LengthOfStay"]
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data["LengthOfStay"].mean()
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+mean_value = borders_data["LengthOfStay"].mean()
+
+print(mean_value)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+mean_value = borders_data["LengthOfStay"].mean()
+
+print(mean_value)
    +
    +
    +
    +

    Frequencies & Crosstabs

    +
      +
    • Frequency: +<df_name>["<col_name>").value_counts()]
      • +
    • Crosstab: +pd.crosstab(<df_name>["<col_1_name>"], <df_name>["<col_2_name>"])
      • +
    • Crosstab & Add Col/Row Totals: +pd.crosstab(<df_name>["<col_1_name>"], <df_name>["<col_2_name>"], margins = True) +tab Create a crosstab for HospitalCode and +Sex, add column and row totals, store the table in a +variable, and print it out. Use the hint button if you need some +help.
      • +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+...pd.crosstab(...)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+crosstab_hospitalcode_sex = pd.crosstab(
+  borders_data["HospitalCode"], borders_data["Sex"]...)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+crosstab_hospitalcode_sex = pd.crosstab(
+  borders_data["HospitalCode"], borders_data["Sex"], margins = True)
+print(crosstab_hospitalcode_sex)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+crosstab_hospitalcode_sex = pd.crosstab(
+  borders_data["HospitalCode"], borders_data["Sex"], margins = True)
+
+print(crosstab_hospitalcode_sex)
    +
    +
    +
    +
    +

    Wrangle – Part 1

    +

    For the next sections, we will focus on using the pandas module +manipulate data and data frames.

    +
    +

    Location

    +

    There are multiple ways to wrangle data in Python - some are more +straightforward than others.

    +

    To begin efficiently locating and manipulating data, it would be +beneficial to become familiar with the concept of locating data in a +dataframe.

    +

    The loc and iloc attributes are used for +indexing, which allows for the locating of data from a dataframe based +on row and column labels/indexes.

    +

    loc - Label-based indexing (such as Hospital Codes, +Names, etc.). iloc - Integer-based indexing (remember +Python indexes count from 0).

    +

    By default, loc and iloc locates row +indexes, instead of column indexes.

    +

    <df_name>.loc['<row_label>'] will select the +first row with row_label as the label index. +<df_name>.iloc['<row_index_label>'] will select +the row at index == row_index_label.

    +

    This does not mean the location attributes are isolated to only +locating rows - in fact, the real power of the location attributes is in +its ability to freely locate relevant rows, columns, values, etc.

    +

    In the next few sections, we will see how useful the location +attributes can be.

    +
    +
    +

    Select / Slice Out Columns

    +

    By far, the most straightforward way to select a column is by the +following method:

    +
      +
    • Selecting/Slicing Out a column: +<df_name>["<col_name>"]
      • +
    +

    However, by using loc and iloc, we can +start to specify further:

    +
      +

    • Selecting/Slicing Out a column: +<df_name>.loc["<row_index>","<column_index>"]

      • +

    • Example: borders_data.loc[:,"Specialty"] will obtain +all the rows from the Specialty column.

      • +
    +

    You may be wondering why using the loc attribute is +helpful here when the first method is much more simple.

    +

    Using loc, you have the ability to select specific rows +within that column

    +
      +
    • Example: borders_data.loc[0:2,"HospitalCode"] will +obtain the first 3 rows from the HospitalCode column.
      • +
    +

    Read in the data stored in the borders_inc_age csv file, select/slice +out the entire HospitalCode column using pandas, and print +out the selected column.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+column_hospital_code =
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+column_hospital_code = borders_data.loc
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+column_hospital_code = borders_data.loc[:,]
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+column_hospital_code = borders_data.loc[:,'HospitalCode']
+
+print(column_hospital_code)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+column_hospital_code = borders_data.loc[:,'HospitalCode']
+
+print(column_hospital_code)
    +
    +
    +
    +

    Select Rows

    +

    The pandas library has multiple way to select rows, depending on your +criteria.

    +
      +

    • Select Rows by index: +<df_name>.iloc[<index_number>]']

      • +

    • Example: `borders_data.iloc[0] This will show the first +row of the dataframe

      • +

    • Select Rows which have a specific value in a specific column: +<df_name>[<df_name>['<column_name>'] == '<row_label>']

      • +

    • Example: +borders_data[borders_data['HospitalCode'] == 'B120H'] +This will show all the rows which have B120H in the +HospitalCode column

      • +

    • Select Rows by index: +<df_name>.loc['<row_label>']']

      • +

    • Example: borders_data.loc['row_label'] This +will the first instance of the row with row_label as the label index - +please note, this csv file does not have row labels

      • +
    +

    Read in the borders_inc_age csv file, and select all the rows with +Specialty == A1.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+rows_of_a1 = borders_data[...]
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+rows_of_a1 = borders_data[borders_data['...']...]
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+rows_of_a1 = borders_data[borders_data['Specialty']=='A1']
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+rows_of_a1 = borders_data[borders_data['Specialty']=='A1']
    +
    +
    +
    +

    Add a New Row/Column

    +

    Adding a new row/column is straightforward.

    +

    When adding a new row, you can first create a dictionary, where +keys (column names) and values (corresponding +values in the new row) are mapped together.

    +
      +
    • Create dictionary of mapped keys (columns) and values (row values): +new_row_data = {'<column_name>':'<row_value>','<column_name>':'<row_value>','<column_name>':'<row_value>'}
      • +
    +

    Then, you can append the new row into the dataframe.

    +

    <df_name> = <df_name>.append(new_row_data)

    +

    If you want to leave the value blank, you can input None +into the row_value.

    +
      +
    • Add new column: +<df_name>['<new_column_name>'] = <row_value>
      • +
    +

    This will create a new column with all the rows populated by the +row_value.

    +

    Read in the borders_inc_age file. Create a new column called +number_of_eyebrows with the row_value as 2 for +all the rows.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data[...] ...
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data['number_of_eyebrows'] = ....
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data['number_of_eyebrows'] = 2
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data['number_of_eyebrows'] = 2
    +
    +
    +
    +

    Delete a Row/Columns

    +

    There are multiple ways to delete rows and columns.

    +

    When deleting a row, you could create a dataframe with the rows +containing certain row_values filtered out:

    +
      +
    • Deleting a row: +<df_name> = <df_name>[<df_name>['<column_name>'] != '<row_value>']
      • +
    +

    You can also delete rows based on their index and row_label by +slicing the locations.

    +
      +
    • Removing the first row: +<df_name> = <df_name>.iloc[1:]
      • +
    +

    If we want to remove only the 2nd row using the same method, you +would need to using locate the specific rows you want to keep, and +append them together.

    +
      +
    • Removing the second row: +<df_name> = <df_name>.iloc[:1].append(<df_name>.iloc[2:]
      • +
    +

    Alternatively, you could use the drop() method.

    +
      +

    • Removing the second row: +<df_name> = <df_name>.drop(<df_name>.index[1])

      • +

    • Removing the second row row_label: +<df_name> = <df_name>.drop('<row_label>', axis=0) +axis=0 specifies you are removing a +row.

      • +

    • Removing the second row by index position: +<df_name> = <df_name>.drop(index_position, axis=0)

      • +
    +

    The drop() method can also be used to drop columns.

    +
      +

    • Removing a column by label: +<df_name> = <df_name>.drop('<column_name>', axis=1) +axis=1 specifies you are removing a +column.

      • +

    • Removing a column by index position: +<df_name> = <df_name>.drop(<df_name>.columns[index_position], axis=1)

      • +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+...pd.crosstab(...)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+crosstab_hospitalcode_sex = pd.crosstab(
+  borders_data["HospitalCode"], borders_data["Sex"]...)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+crosstab_hospitalcode_sex = pd.crosstab(
+  borders_data["HospitalCode"], borders_data["Sex"], margins = True)
+print(crosstab_hospitalcode_sex)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+crosstab_hospitalcode_sex = pd.crosstab(
+  borders_data["HospitalCode"], borders_data["Sex"], margins = True)
+
+print(crosstab_hospitalcode_sex)
    +
    +

    ##Wrangle – Part 2

    +

    · Manipulate Strings

    +

    · Aggregate Dataframe

    +

    · Merge Dataframe

    +
    +

    Help & Feedback

    @@ -987,16 +1400,16 @@

    Feedback

    @@ -1057,10 +1470,339 @@

    Feedback

    fig.num = 0, exercise.df_print = "paged"), engine = "r", version = "4"), class = c("r", "tutorial_exercise"))) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    From 495d3542c46674a22a5cd3dcdec331af2c4c3d41 Mon Sep 17 00:00:00 2001 From: vly12 Date: Thu, 21 Mar 2024 18:14:42 +0000 Subject: [PATCH 06/15] Wrangle #2 beginning --- intro.Rmd | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 61 insertions(+), 3 deletions(-) diff --git a/intro.Rmd b/intro.Rmd index 216183f..60a88f8 100755 --- a/intro.Rmd +++ b/intro.Rmd @@ -741,11 +741,69 @@ grade_code() ##Wrangle – Part 2 -· Manipulate Strings +### Manipulate Strings -· Aggregate Dataframe +There are a number of ways to manipulate strings in Python. Here are a few methods to achieve various outputs: -· Merge Dataframe +* Split values at a delimiter: `[''] = [''].str.split('')` + +*Example: `borders_data['ManagementofPatient'] = borders_data['ManagementofPatient'].str.split('a')` + +After the above line of code, if you `print(borders_data)`, you will see that all the values in the column `ManagementofPatient` has been split at the value 'a'. + +* Strip values of leading and trailing white space: `[''] = [''].str.strip()` +* Convert values to upper case: `[''] = [''].str.upper()` +* Convert values to lower case: `[''] = [''].str.lower()` +* Replace values: `[''] = [''].str.replace('','')` + +It is important to note that this is not an exhaustive list of all the string manipulation methods - there are many more to find out and use, depending on your specification requirements. + +In the exercise below, read the borders_inc_age csv in, then replace all instances of `B120H` in the `HospitalCode` column with the phrase `BRILLIANT`. + +```{python manipulate-string, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python manipulate-string-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data['HospitalCode'] = ... +``` + +```{python manipulate-string-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +borders_data['HospitalCode'] = borders_data['HospitalCode']... +``` + +```{python manipulate-string-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT') +``` + +```{python manipulate-string-solution} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT') +``` + +```{python manipulate-string-check} +grade_code() +``` + +### Aggregate Dataframe + +### Merge Dataframe ## Help & Feedback From dcb6ab88ae486acdeaf52ef9d8f805f34120f6b3 Mon Sep 17 00:00:00 2001 From: vly12 Date: Thu, 28 Mar 2024 19:49:53 +0000 Subject: [PATCH 07/15] Added new dataframes in order to illustrate merging/joining. Added joining image. Completed sections on groupby and aggregate. --- data/Baby5.csv | 9 + data/Baby6.csv | 5 + images/join_python.png | Bin 0 -> 73465 bytes intro.Rmd | 124 ++++++++++++- intro.html | 406 +++++++++++++++++++++++++++++++++++++---- 5 files changed, 507 insertions(+), 37 deletions(-) create mode 100644 data/Baby5.csv create mode 100644 data/Baby6.csv create mode 100644 images/join_python.png diff --git a/data/Baby5.csv b/data/Baby5.csv new file mode 100644 index 0000000..fea1a5c --- /dev/null +++ b/data/Baby5.csv @@ -0,0 +1,9 @@ +FAMILYID,BABYNAME,DOB +1,Julie,1/15/2017 +2,David,1/1/2017 +4,Caroline,1/6/2017 +3,Lucy,1/29/2017 +1,Kevin,1/15/2017 +1,Stephen,1/15/2017 +2,Declan,1/1/2017 +4,Sara,1/6/2017 diff --git a/data/Baby6.csv b/data/Baby6.csv new file mode 100644 index 0000000..483aef9 --- /dev/null +++ b/data/Baby6.csv @@ -0,0 +1,5 @@ +FAMILYID,DOB,SURNAME +2,1/1/2017,Smith +3,1/29/2017,Jones +1,1/15/2017,McDaid +4,1/6/2017,MacArthur diff --git a/images/join_python.png 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zuU_Oim;czZF;)79S{N@wv6!ZS>spQfuML_qJ48JG9_p_yE!AIV`C&H4vrSCETOD#= r-DiYWvlAX2{7Tb$md4dMvhg45L+8~N=B>0@#{dMLu6{1-oD!M `iloc` - Integer-based indexing (remember Python indexes count from 0). By default, `loc` and `iloc` locates row indexes, instead of column indexes. -`.loc['']` will select the first row with row_label as the label index. +`.loc['']` will select the first row with row_label as the label index.
    `.iloc['']` will select the row at index == row_index_label. This does not mean the location attributes are isolated to only locating rows - in fact, the real power of the location attributes is in its ability to freely locate relevant rows, columns, values, etc. +It would further be helpful to remember that in some methods, the `axis` stated will indicate whether it is targetting a row or a column. + +`axis = 0` = row
    +`axis = 1` = column + In the next few sections, we will see how useful the location attributes can be. @@ -614,7 +619,7 @@ When adding a new row, you can first create a dictionary, where `keys` (column n Then, you can append the new row into the dataframe. -` = .append(new_row_data)` +* ` = .append(new_row_data)` If you want to leave the value blank, you can input `None` into the row_value. @@ -739,7 +744,7 @@ grade_code() ``` -##Wrangle – Part 2 +## Wrangle – Part 2 ### Manipulate Strings @@ -747,7 +752,7 @@ There are a number of ways to manipulate strings in Python. Here are a few metho * Split values at a delimiter: `[''] = [''].str.split('')` -*Example: `borders_data['ManagementofPatient'] = borders_data['ManagementofPatient'].str.split('a')` +* Example: `borders_data['ManagementofPatient'] = borders_data['ManagementofPatient'].str.split('a')` After the above line of code, if you `print(borders_data)`, you will see that all the values in the column `ManagementofPatient` has been split at the value 'a'. @@ -801,10 +806,119 @@ borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H', grade_code() ``` +### Group By + +The `groupby()` function in Pandas allows us to split a Dataframe into group based on one or more keys, such as column values, and then perform operations on them, such as aggregations or transformations. + +* `.groupby('')` + +Read in the `borders_inc_age` csv. Then, apply the `groupby()` function to the `HospitalCode` column. + +```{python groupby, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python groupby-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.groupby('HospitalCode') +``` + +```{python groupby-check} +grade_code() +``` + + ### Aggregate Dataframe +With the `groupby()` function, we can apply the aggregate `agg()` function in conduct operations on specific columns. + +* `.agg({<'column'> : <'function'>})` + +Multiple operations can also be carried out. + +* `.agg({<'column'> : [<'function1'>, <'function2'>,<'function3'>]})` + +Multiple columns can aggregated. + +* `.agg({<'column1'> : [<'function1'>, <'function2'>,<'function3'>],<'column2'> : [<'function1'>, <'function2'>,<'function3'>]})` + +For example, let's store the above `borders_data.groupby('HospitalCode')` into a variable called `grouped_by_hospital_code`. + +We can now find the `mean`, `max`, `count` and `sum` of the `LengthOfStay` variables, grouped by `HospitalCode`. + +* `grouped_by_hospital_code.agg({'LengthOfStay': ['mean','max','count','sum']})` + +We could also more easily use the `describe()` function within the `agg()` function detailed above. + +Give that a go: Read in the `borders_inc_age` csv file, then group by `HospitalCode`, storing that into a variable called `grouped_by_hospital_code`. Aggregate using the method above. Use the `describe()` function to find important values from the `LengthOfStay` column. + +```{python aggregate, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python aggregate-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +group_by_hospital_code = borders_data.groupby('HospitalCode') + +... +``` + +```{python aggregate-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +group_by_hospital_code = borders_data.groupby('HospitalCode') + +group_by_hospital_code.agg( + ...) +``` + +```{python aggregate-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +group_by_hospital_code = borders_data.groupby('HospitalCode') + +group_by_hospital_code.agg( + {'LengthOfStay':'describe'}) + +``` + +```{python aggregate-solution} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +group_by_hospital_code = borders_data.groupby('HospitalCode') + +group_by_hospital_code.agg( + {'LengthOfStay':'describe'}) + +``` + +```{python aggregate-check} +grade_code() +``` + + ### Merge Dataframe + + +```{r join-diagrams-overview, echo=FALSE, fig.align='center', out.width="100%"} +knitr::include_graphics("images/join_python.png") +``` + + ## Help & Feedback #### Feedback diff --git a/intro.html b/intro.html index db3af2c..68f8dd8 100644 --- a/intro.html +++ b/intro.html @@ -624,17 +624,21 @@

    Location

    indexing, which allows for the locating of data from a dataframe based on row and column labels/indexes.

    loc - Label-based indexing (such as Hospital Codes, -Names, etc.). iloc - Integer-based indexing (remember +Names, etc.).
    iloc - Integer-based indexing (remember Python indexes count from 0).

    By default, loc and iloc locates row indexes, instead of column indexes.

    <df_name>.loc['<row_label>'] will select the -first row with row_label as the label index. +first row with row_label as the label index.
    <df_name>.iloc['<row_index_label>'] will select the row at index == row_index_label.

    This does not mean the location attributes are isolated to only locating rows - in fact, the real power of the location attributes is in its ability to freely locate relevant rows, columns, values, etc.

    +

    It would further be helpful to remember that in some methods, the +axis stated will indicate whether it is targetting a row or +a column.

    +

    axis = 0 = row
    axis = 1 = column

    In the next few sections, we will see how useful the location attributes can be.

    @@ -713,19 +717,19 @@

    Select Rows

    • Select Rows by index: <df_name>.iloc[<index_number>]']

      • -

    • Example: `borders_data.iloc[0] This will show the first -row of the dataframe

      • +

    • Example: borders_data.iloc[0] This will show the +first row of the dataframe

    • Select Rows which have a specific value in a specific column: <df_name>[<df_name>['<column_name>'] == '<row_label>']

    • Example: borders_data[borders_data['HospitalCode'] == 'B120H'] -This will show all the rows which have B120H in the -HospitalCode column

      • +This will show all the rows which have B120H in the +HospitalCode column

    • Select Rows by index: <df_name>.loc['<row_label>']']

      • -

    • Example: borders_data.loc['row_label'] This -will the first instance of the row with row_label as the label index - -please note, this csv file does not have row labels

      • +

    • Example: borders_data.loc['row_label'] This will +locate the first instance of the row with row_label as the label index - +please note, this csv file does not have row labels

    Read in the borders_inc_age csv file, and select all the rows with Specialty == A1.

    @@ -775,7 +779,9 @@

    Add a New Row/Column

    new_row_data = {'<column_name>':'<row_value>','<column_name>':'<row_value>','<column_name>':'<row_value>'}

    Then, you can append the new row into the dataframe.

    -

    <df_name> = <df_name>.append(new_row_data)

    +
      +
    • <df_name> = <df_name>.append(new_row_data)
      • +

    If you want to leave the value blank, you can input None into the row_value.

      @@ -864,6 +870,8 @@

      Delete a Row/Columns

    • Removing a column by index position: <df_name> = <df_name>.drop(<df_name>.columns[index_position], axis=1)

    +

    Read in the borders_inc_age csv. Remove all the rows which have +Male as the value in the Sex column.

    @@ -875,39 +883,214 @@

    Delete a Row/Columns

    data-lines="0" data-pipe="|>"> 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-...pd.crosstab(...)
    +borders_data = ... 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-crosstab_hospitalcode_sex = pd.crosstab(
-  borders_data["HospitalCode"], borders_data["Sex"]...)
    +borders_data = borders_data[...] 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-crosstab_hospitalcode_sex = pd.crosstab(
-  borders_data["HospitalCode"], borders_data["Sex"], margins = True)
-print(crosstab_hospitalcode_sex)
    +borders_data = borders_data[borders_data[...] != ...] 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-crosstab_hospitalcode_sex = pd.crosstab(
-  borders_data["HospitalCode"], borders_data["Sex"], margins = True)
+borders_data = borders_data[borders_data['Sex'] != 'Male']
    +
    +
    + +
    +

    Wrangle – Part 2

    +
    +

    Manipulate Strings

    +

    There are a number of ways to manipulate strings in Python. Here are +a few methods to achieve various outputs:

    +
      +

    • Split values at a delimiter: +<df_name>['<column_name>'] = <df_name>['<column_name>'].str.split('<delimiter>')

      • +

    • Example: +borders_data['ManagementofPatient'] = borders_data['ManagementofPatient'].str.split('a')

      • +
    +

    After the above line of code, if you +print(borders_data), you will see that all the values in +the column ManagementofPatient has been split at the value +‘a’.

    +
      +
    • Strip values of leading and trailing white space: +<df_name>['<column_name>'] = <df_name>['<column_name>'].str.strip()
      • +
    • Convert values to upper case: +<df_name>['<column_name>'] = <df_name>['<column_name>'].str.upper()
      • +
    • Convert values to lower case: +<df_name>['<column_name>'] = <df_name>['<column_name>'].str.lower()
      • +
    • Replace values: +<df_name>['<column_name>'] = <df_name>['<column_name>'].str.replace('<old_value>','<new_value>')
      • +
    +

    It is important to note that this is not an exhaustive list of all +the string manipulation methods - there are many more to find out and +use, depending on your specification requirements.

    +

    In the exercise below, read the borders_inc_age csv in, then replace +all instances of B120H in the HospitalCode +column with the phrase BRILLIANT.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-print(crosstab_hospitalcode_sex)
    +borders_data['HospitalCode'] = ... +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+
+borders_data['HospitalCode'] = borders_data['HospitalCode']...
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+
+borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT')
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT')
    +
    +
    +
    +

    Group By

    +

    The groupby() function in Pandas allows us to split a +Dataframe into group based on one or more keys, such as column values, +and then perform operations on them, such as aggregations or +transformations.

    +
      +
    • <dataframe>.groupby('<column_name>')
      • +
    +

    Read in the borders_inc_age csv. Then, apply the +groupby() function to the HospitalCode +column.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data.groupby('HospitalCode')
    -

    ##Wrangle – Part 2

    -

    · Manipulate Strings

    -

    · Aggregate Dataframe

    -

    · Merge Dataframe

    +
    +
    +

    Aggregate Dataframe

    +

    With the groupby() function, we can apply the aggregate +agg() function in conduct operations on specific +columns.

    +
      +
    • <grouped_variable>.agg({<'column'> : <'function'>})
      • +
    +

    Multiple operations can also be carried out.

    +
      +
    • <grouped_variable>.agg({<'column'> : [<'function1'>, <'function2'>,<'function3'>]})
      • +
    +

    Multiple columns can aggregated.

    +
      +
    • <grouped_variable>.agg({<'column1'> : [<'function1'>, <'function2'>,<'function3'>],<'column2'> : [<'function1'>, <'function2'>,<'function3'>]})
      • +
    +

    For example, let’s store the above +borders_data.groupby('HospitalCode') into a variable called +grouped_by_hospital_code.

    +

    We can now find the mean, max, +count and sum of the LengthOfStay +variables, grouped by HospitalCode.

    +
      +
    • grouped_by_hospital_code.agg({'LengthOfStay': ['mean','max','count','sum']})
      • +
    +

    We could also more easily use the describe() function +within the agg() function detailed above.

    +

    Give that a go: Read in the borders_inc_age csv file, +then group by HospitalCode, storing that into a variable +called grouped_by_hospital_code. Aggregate using the method +above. Use the describe() function to find important values +from the LengthOfStay column.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+group_by_hospital_code = borders_data.groupby('HospitalCode')
+
+...
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+group_by_hospital_code = borders_data.groupby('HospitalCode')
+
+group_by_hospital_code.agg(
+  ...)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+group_by_hospital_code = borders_data.groupby('HospitalCode')
+
+group_by_hospital_code.agg(
+  {'LengthOfStay':'describe'})
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+group_by_hospital_code = borders_data.groupby('HospitalCode')
+
+group_by_hospital_code.agg(
+  {'LengthOfStay':'describe'})
    +
    +
    +
    +

    Merge Dataframe

    +

    series = pd.Series([10,20,30,40], name=‘New_Column’)

    +

    df = pd.read_csv(“your_file.csv”)

    +

    merged_df = df.merge(series,how = ‘right’)

    +

    @@ -1400,16 +1583,16 @@

    Feedback

    @@ -1772,10 +1955,9 @@

    Feedback

    engine = "python")), code_check = NULL, error_check = NULL, check = structure("grade_code()", chunk_opts = list(label = "delete-row-check", engine = "python")), solution = structure(c("", "borders_data = pd.read_csv(\"data/borders_inc_age.csv\")", - "", "crosstab_hospitalcode_sex = pd.crosstab(", " borders_data[\"HospitalCode\"], borders_data[\"Sex\"], margins = True)", - "", "print(crosstab_hospitalcode_sex)"), chunk_opts = list( - label = "delete-row-solution", engine = "python")), tests = NULL, - options = list(eval = FALSE, echo = TRUE, results = "markup", + "", "borders_data = borders_data[borders_data['Sex'] != 'Male']" + ), chunk_opts = list(label = "delete-row-solution", engine = "python")), + tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE, size = "normalsize", background = "#F7F7F7", strip.white = TRUE, cache = 0, @@ -1799,10 +1981,170 @@

    Feedback

    exercise.df_print = "paged"), engine = "python", version = "4"), class = c("python", "tutorial_exercise"))) + + + + + + + + + + + + + + +

    From d5b9d1f26a9b53d2a59fe54fba50e2ca4cc6313b Mon Sep 17 00:00:00 2001 From: vly12 Date: Fri, 29 Mar 2024 21:12:57 +0000 Subject: [PATCH 08/15] Completing merge section. Adding rename and recode section. --- intro.Rmd | 185 +++++++++++++++++++++++++++++++++++++- intro.html | 257 ++++++++++++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 425 insertions(+), 17 deletions(-) diff --git a/intro.Rmd b/intro.Rmd index 56cf42f..aa539c2 100755 --- a/intro.Rmd +++ b/intro.Rmd @@ -629,7 +629,7 @@ If you want to leave the value blank, you can input `None` into the row_value. This will create a new column with all the rows populated by the row_value. -Read in the borders_inc_age file. Create a new column called `number_of_eyebrows` with the row_value as `2` for all the rows. +Read in the `borders_inc_age` csv file. Create a new column called `number_of_eyebrows` with the row_value as `2` for all the rows. ```{python new-row, exercise = TRUE, exercise.setup = "pandas-setup"} @@ -683,7 +683,7 @@ You can also delete rows based on their index and row_label by slicing the locat * Removing the first row: ` = .iloc[1:]` -If we want to remove only the 2nd row using the same method, you would need to using locate the specific rows you want to keep, and append them together. +If we want to remove only the 2nd row using the same method, you would need to locate the specific rows you want to keep, and append them together. * Removing the second row: ` = .iloc[:1].append(.iloc[2:]` @@ -783,7 +783,6 @@ borders_data['HospitalCode'] = ... borders_data = pd.read_csv("data/borders_inc_age.csv") - borders_data['HospitalCode'] = borders_data['HospitalCode']... ``` @@ -791,7 +790,6 @@ borders_data['HospitalCode'] = borders_data['HospitalCode']... borders_data = pd.read_csv("data/borders_inc_age.csv") - borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT') ``` @@ -806,6 +804,113 @@ borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H', grade_code() ``` +### Recode + +Another way to recode data which offers more flexibility is by using the `loc` attribute. + +* `.loc[[''] == , ''] = ` + +This method uses the boolean operator `==`, which means **equal to**. + +This operator can be substituted with others. + +For example, in the case that you wish to find all the values which are less than `5` in a given column, and replace them with `Small`, you would code the following: + +* `data_frame.loc[data_frame['column1'] < 5, 'column1'] = 'Small'` + +As you can imagine, this methods affords a great level of versatility in data analysis. + +In the exercise below, read in the `borders_inc_age` csv file, and recode all instances of `B120H` in the `HospitalCode` column to `Recoded`. + + +```{python recode, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python recode-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.loc[borders_data[...] = ... +``` + +```{python recode-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.loc[borders_data['HospitalCode'] == ..., ...] = 'Replaced' +``` + +```{python recode-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.loc[borders_data['HospitalCode'] == 'B120H', 'HospitalCode'] = 'Replaced' +``` + +```{python recode-solution} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.loc[borders_data['HospitalCode'] == 'B120H', 'HospitalCode'] = 'Replaced' +``` + +```{python recode-check} +grade_code() +``` + +### Rename + +Renames specific columns in a data frame: + +* `.rename(columns={'':''})` + +The column `HospitalCode` in `borders_data` doesn't follow the style guide, rename it to `Hospital_Code`. + + +```{python rename, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + + +``` + +```{python rename-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.rename(...) +``` + +```{python rename-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.rename(columns={...}) +``` + +```{python rename-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.rename(columns={'HospitalCode':'Hospital_Code'}) +``` + +```{python rename-solution} + +borders_data = pd.read_csv("data/borders_inc_age.csv") + +borders_data.rename(columns={'HospitalCode':'Hospital_Code'}) +``` + +```{python rename-check} +grade_code() +``` + + ### Group By The `groupby()` function in Pandas allows us to split a Dataframe into group based on one or more keys, such as column values, and then perform operations on them, such as aggregations or transformations. @@ -919,6 +1024,78 @@ knitr::include_graphics("images/join_python.png") ``` +* `left` - taking all records from the "left", `data_frame_1`, and adding matched records from the "right", `data_frame_2`, introducing `NaN` where the "right", `data_2` had no matching records +* `right` - taking all records from the "right", `data_frame_2`, and adding matched records from the "left", `data_frame_1`, introducing `NaN` where the "left", `data_frame_1` had no matching records +* `inner` - producing only records where there were matches on both given data sets +* `full` - retaining all records from both data sets and introducing `na` on either one where there was no matching records + +By default, the merge type will be `inner` unless stated otherwise. + +You define the merge/join type by defining the `how = ''`. + +You should further define what common columns/variables the data frames should merge `on` by defining the `on = ['']` + +* `.merge(, how = '', on = ['' , ''])` + +2 new data-sets have been loaded, `baby5` and `baby6`, these have common variables `FAMILYID` and `DOB`. Using a `left` join, merge them together. + + +```{python merge, exercise = TRUE, exercise.setup = "pandas-setup"} + +borders_data = pd.read_csv("data/borders_inc_age.csv") +baby5 = pd.read_csv("data/Baby5.csv") +baby6 = pd.read_csv("data/Baby6.csv") + + + +``` + +```{python merge-hint-1} + +borders_data = pd.read_csv("data/borders_inc_age.csv") +baby5 = pd.read_csv("data/Baby5.csv") +baby6 = pd.read_csv("data/Baby6.csv") + +baby5.merge(...) + +``` + +```{python merge-hint-2} + +borders_data = pd.read_csv("data/borders_inc_age.csv") +baby5 = pd.read_csv("data/Baby5.csv") +baby6 = pd.read_csv("data/Baby6.csv") + +baby5.merge(baby6, how = '...', on = [...]) + +``` + +```{python merge-hint-3} + +borders_data = pd.read_csv("data/borders_inc_age.csv") +baby5 = pd.read_csv("data/Baby5.csv") +baby6 = pd.read_csv("data/Baby6.csv") + +baby5.merge(baby6, how = 'left', on = ['FAMILYID','DOB']) + +``` + +```{python merge-solution} + +borders_data = pd.read_csv("data/borders_inc_age.csv") +baby5 = pd.read_csv("data/Baby5.csv") +baby6 = pd.read_csv("data/Baby6.csv") + +baby5.merge(baby6, how = 'left', on = ['FAMILYID','DOB']) + +``` + +```{python merge-check} +grade_code() +``` + + + ## Help & Feedback #### Feedback diff --git a/intro.html b/intro.html index 68f8dd8..a4c3012 100644 --- a/intro.html +++ b/intro.html @@ -790,9 +790,9 @@

    Add a New Row/Column

    This will create a new column with all the rows populated by the row_value.

    -

    Read in the borders_inc_age file. Create a new column called -number_of_eyebrows with the row_value as 2 for -all the rows.

    +

    Read in the borders_inc_age csv file. Create a new +column called number_of_eyebrows with the row_value as +2 for all the rows.

    @@ -844,8 +844,8 @@

    Delete a Row/Columns

    <df_name> = <df_name>.iloc[1:]

    If we want to remove only the 2nd row using the same method, you -would need to using locate the specific rows you want to keep, and -append them together.

    +would need to locate the specific rows you want to keep, and append them +together.

    • Removing the second row: <df_name> = <df_name>.iloc[:1].append(<df_name>.iloc[2:]
      • @@ -981,6 +981,110 @@

      Manipulate Strings

      borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT')
    +
    +

    Recode

    +

    Another way to recode data which offers more flexibility is by using +the loc attribute.

    +
      +
    • <df_name>.loc[<df_name>['<column_name>'] == <existing_value>, '<column_name>'] = <new_value>
      • +
    +

    This method uses the boolean operator ==, which means +equal to.

    +

    This operator can be substituted with others.

    +

    For example, in the case that you wish to find all the values which +are less than 5 in a given column, and replace them with +Small, you would code the following:

    +
      +
    • data_frame.loc[data_frame['column1'] < 5, 'column1'] = 'Small'
      • +
    +

    As you can imagine, this methods affords a great level of versatility +in data analysis.

    +

    In the exercise below, read in the borders_inc_age csv +file, and recode all instances of B120H in the +HospitalCode column to Recoded.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data.loc[borders_data[...] = ...
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+
+borders_data.loc[borders_data['HospitalCode'] == ..., ...] = 'Replaced'
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+
+borders_data.loc[borders_data['HospitalCode'] == 'B120H', 'HospitalCode'] = 'Replaced'
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data.loc[borders_data['HospitalCode'] == 'B120H', 'HospitalCode'] = 'Replaced'
    +
    +
    +
    +

    Rename

    +

    Renames specific columns in a data frame:

    +
      +
    • <data_frame>.rename(columns={'<old_column_name>':'<new_column_name>'})
      • +
    +

    The column HospitalCode in borders_data +doesn’t follow the style guide, rename it to +Hospital_Code.

    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    + +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data.rename(...)
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+
+borders_data.rename(columns={...})
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+
+borders_data.rename(columns={'HospitalCode':'Hospital_Code'})
    +
    +
    +
    borders_data = pd.read_csv("data/borders_inc_age.csv")
+
+borders_data.rename(columns={'HospitalCode':'Hospital_Code'})
    +
    +

    Group By

    The groupby() function in Pandas allows us to split a @@ -1087,10 +1191,29 @@

    Aggregate Dataframe

    Merge Dataframe

    -

    series = pd.Series([10,20,30,40], name=‘New_Column’)

    -

    df = pd.read_csv(“your_file.csv”)

    -

    merged_df = df.merge(series,how = ‘right’)

    +
      +
    • left - taking all records from the “left”, +data_1, and adding matched records from the “right”, +data_2, introducing NaN where the “right”, +data_2 had no matching records
      • +
    • right - taking all records from the “right”, +data_2, and adding matched records from the “left”, +data_1, introducing NaN where the “left”, +data_1 had no matching records
      • +
    • inner - producing only records where there were matches +on both given data sets
      • +
    • full - retaining all records from both data sets and +introducing na on either one where there was no matching +records
      • +
    +

    By default, the merge type will be inner unless stated +otherwise.

    +

    You define the merge/join type by defining the +how = '<type>'.

    +
      +
    • +
    @@ -1583,16 +1706,16 @@

    Feedback

    @@ -2037,6 +2160,114 @@

    Feedback

    + + + + + + + + + + From 3ad6c511afe6e0f60ff189625b06945f5f57c17f Mon Sep 17 00:00:00 2001 From: vly12 Date: Fri, 29 Mar 2024 21:34:53 +0000 Subject: [PATCH 09/15] Deleting random lines --- intro.html | 138 ++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 120 insertions(+), 18 deletions(-) diff --git a/intro.html b/intro.html index a4c3012..f0aa5b3 100644 --- a/intro.html +++ b/intro.html @@ -960,7 +960,6 @@

    Manipulate Strings

    data-pipe="|>"> 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-
 borders_data['HospitalCode'] = borders_data['HospitalCode']...
    Manipulate Strings data-pipe="|>"> 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-
 borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT')
    Recode data-lines="0" data-pipe="|>"> 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-
 borders_data.loc[borders_data['HospitalCode'] == ..., ...] = 'Replaced'
    Recode data-lines="0" data-pipe="|>"> 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-
 borders_data.loc[borders_data['HospitalCode'] == 'B120H', 'HospitalCode'] = 'Replaced'
    Rename data-lines="0" data-pipe="|>"> 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-
 borders_data.rename(columns={...})
    Rename data-lines="0" data-pipe="|>"> 
    borders_data = pd.read_csv("data/borders_inc_age.csv")
 
-
 borders_data.rename(columns={'HospitalCode':'Hospital_Code'})
    Merge Dataframe

    • left - taking all records from the “left”, -data_1, and adding matched records from the “right”, -data_2, introducing NaN where the “right”, -data_2 had no matching records
      • +data_frame_1, and adding matched records from the “right”, +data_frame_2, introducing NaN where the +“right”, data_2 had no matching records
    • right - taking all records from the “right”, -data_2, and adding matched records from the “left”, -data_1, introducing NaN where the “left”, -data_1 had no matching records
      • +data_frame_2, and adding matched records from the “left”, +data_frame_1, introducing NaN where the +“left”, data_frame_1 had no matching records
    • inner - producing only records where there were matches on both given data sets
    • full - retaining all records from both data sets and @@ -1211,9 +1205,60 @@

      Merge Dataframe

      otherwise.

      You define the merge/join type by defining the how = '<type>'.

      +

      You should further define what common columns/variables the data +frames should merge on by defining the +on = ['<insert_column>']

        -
      • +
      • <data_frame_1>.merge(<data_frame_2>, how = '<join_type>', on = ['<insert_column_1>' , '<insert_column_2>'])
      +

      2 new data-sets have been loaded, baby5 and +baby6, these have common variables FAMILYID +and DOB. Using a left join, merge them +together.

      +
      +
      borders_data = pd.read_csv("data/borders_inc_age.csv")
+baby5 = pd.read_csv("data/Baby5.csv")
+baby6 = pd.read_csv("data/Baby6.csv")
      + +
      +
      +
      borders_data = pd.read_csv("data/borders_inc_age.csv")
+baby5 = pd.read_csv("data/Baby5.csv")
+baby6 = pd.read_csv("data/Baby6.csv")
+
+baby5.merge(...)
      +
      +
      +
      borders_data = pd.read_csv("data/borders_inc_age.csv")
+baby5 = pd.read_csv("data/Baby5.csv")
+baby6 = pd.read_csv("data/Baby6.csv")
+
+baby5.merge(baby6, how = '...', on = [...])
      +
      +
      +
      borders_data = pd.read_csv("data/borders_inc_age.csv")
+baby5 = pd.read_csv("data/Baby5.csv")
+baby6 = pd.read_csv("data/Baby6.csv")
+
+baby5.merge(baby6, how = 'left', on = ['FAMILYID','DOB'])
      +
      +
      +
      borders_data = pd.read_csv("data/borders_inc_age.csv")
+baby5 = pd.read_csv("data/Baby5.csv")
+baby6 = pd.read_csv("data/Baby6.csv")
+
+baby5.merge(baby6, how = 'left', on = ['FAMILYID','DOB'])
      +
    @@ -1706,16 +1751,16 @@

    Feedback

    @@ -2372,10 +2417,67 @@

    Feedback

    exercise.df_print = "paged"), engine = "python", version = "4"), class = c("python", "tutorial_exercise"))) + + + + +

    From 6e323c5205caf29b1b4fa9747915348eb5238f89 Mon Sep 17 00:00:00 2001 From: vly12 Date: Mon, 1 Apr 2024 11:54:54 +0100 Subject: [PATCH 10/15] Indent --- intro.Rmd | 1 + 1 file changed, 1 insertion(+) diff --git a/intro.Rmd b/intro.Rmd index aa539c2..007ee46 100755 --- a/intro.Rmd +++ b/intro.Rmd @@ -410,6 +410,7 @@ grade_code() * Crosstab: `pd.crosstab([""], [""])` * Crosstab & Add Col/Row Totals: `pd.crosstab([""], [""], margins = True)` tab + Create a crosstab for `HospitalCode` and `Sex`, add column and row totals, store the table in a variable, and print it out. Use the hint button if you need some help. ```{python freq, exercise = TRUE, exercise.setup = "pandas-setup"} From fe6aa96bc2ad17401efbd663bd09bbab7e2a9ebd Mon Sep 17 00:00:00 2001 From: vly12 Date: Tue, 16 Apr 2024 17:01:22 +0100 Subject: [PATCH 11/15] Adding new sections --- intro.Rmd | 33 +++++++++++++++++++++++++++++++++ intro.html | 38 ++++++++++++++++++++++---------------- 2 files changed, 55 insertions(+), 16 deletions(-) diff --git a/intro.Rmd b/intro.Rmd index 007ee46..5753120 100755 --- a/intro.Rmd +++ b/intro.Rmd @@ -28,6 +28,7 @@ tutorial_options( ) ``` + ```{python pandas-setup, include = FALSE, eval = FALSE} # Import pandas library import pandas as pd @@ -57,6 +58,33 @@ Welcome to an introduction to python. This course is designed as a self-led intr Python is a powerful general purpose programming language with widespread use in many application domains. Python is open source and free to use, and available for all major operating systems. +### How does Python run? + +Traditionally, when a Python script is run, the entire script is interpreted and run from the top down. + +```{python runtime, eval = FALSE, echo = TRUE} +a = 15 +b = 10 +c = a + b +print(c) +``` + +When the above Python script is run, the entire script will be run from the top down, resulting in an output of `25`. + +There are a number of advantages to having Python scripts run this way. To name a few: + +* The script has a sequential flow +* It is easier to debug +* It is clearer and more readable + +However, there are a number of ways to run individual lines of code within a Python script, should that be preferred. In this course, we will touch upon `Jupyter Notebook` in the *IDE* section, where this can be accomplished. + + + +## IDE + +Within this section, we will cover two IDEs which can be used to create and run Python scripts. + ### Jupyter Notebook There are several environments where you can run Python codes, such as "JupyterLab", "Jupyter Notebook". In this training course, we will use “Jupyter Notebook”. @@ -159,6 +187,9 @@ knitr::include_graphics("images/markdown-example2.PNG") Python library is a collection of functions and methods that allows you to perform lots of actions without writing your own code. For example, “pandas” is a Python library for data manipulation and analysis, which is used a lot in this training guidance. + +### VS Code + ## Foundations This section will walk you through the key data types and structures necessary to hit the ground running and start using Python productively. @@ -356,6 +387,8 @@ print(hello_world) * `describe()` returns all summary statistics based on a given array. +For example, `[""].median()` will generate the `median` value of the values within the stated column. + In the exercise below, you have the borders data-set loaded as `borders_data`. See if you can get the mean value for `LengthOfStay`, store it in a variable, and print that variable. Use the hint button if you need some help. ```{python mean, exercise = TRUE, exercise.setup = "pandas-setup"} diff --git a/intro.html b/intro.html index f0aa5b3..2a65a14 100644 --- a/intro.html +++ b/intro.html @@ -322,11 +322,15 @@

    Strings

    -
    multiline = '''White space is
-preserved
-in multiline strings.'''
+
    def greet(name):
+    print(f"Hello, {name}!")
 
-multiline
    
+def main():
+    user_name = input("Enter your name: ")
+    greet(user_name)
+
+if __name__ == "__main__":
+    main()

    The print() function interprets these escape characters @@ -562,11 +566,11 @@

    Frequencies & Crosstabs

    pd.crosstab(<df_name>["<col_1_name>"], <df_name>["<col_2_name>"])
  • Crosstab & Add Col/Row Totals: pd.crosstab(<df_name>["<col_1_name>"], <df_name>["<col_2_name>"], margins = True) -tab Create a crosstab for HospitalCode and -Sex, add column and row totals, store the table in a -variable, and print it out. Use the hint button if you need some -help.
    • +tab +

    Create a crosstab for HospitalCode and Sex, +add column and row totals, store the table in a variable, and print it +out. Use the hint button if you need some help.

    @@ -1324,7 +1328,7 @@

    Feedback

    "library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, - chunks = list(list(label = "strings2", code = "multiline = '''White space is\npreserved\nin multiline strings.'''\n\nmultiline", + chunks = list(list(label = "strings2", code = "def greet(name):\n print(f\"Hello, {name}!\")\n\ndef main():\n user_name = input(\"Enter your name: \")\n greet(user_name)\n\nif __name__ == \"__main__\":\n main()\n", opts = list(label = "\"strings2\"", exercise = "TRUE", engine = "\"python\""), engine = "python")), code_check = NULL, error_check = NULL, check = NULL, solution = NULL, tests = NULL, @@ -1346,9 +1350,11 @@

    Feedback

    engine = "python", split = FALSE, include = TRUE, purl = TRUE, max.print = 1000, exercise.timelimit = 60, exercise.checker = "function (...) \n{\n lifecycle::deprecate_soft(\"0.2.2\", \"grade_learnr()\", \"gradethis_exercise_checker()\")\n gradethis_exercise_checker(...)\n}", exercise.error.check.code = "gradethis_error_checker()", - label = "strings2", exercise = TRUE, code = c("multiline = '''White space is", - "preserved", "in multiline strings.'''", "", "multiline" - ), out.width.px = 624, out.height.px = 384, params.src = "strings2, exercise = TRUE, engine=\"python\"", + label = "strings2", exercise = TRUE, code = c("def greet(name):", + " print(f\"Hello, {name}!\")", "", "def main():", + " user_name = input(\"Enter your name: \")", " greet(user_name)", + "", "if __name__ == \"__main__\":", " main()", ""), + out.width.px = 624, out.height.px = 384, params.src = "strings2, exercise = TRUE, engine=\"python\"", exercise.df_print = "paged"), engine = "python", version = "4"), class = c("python", "tutorial_exercise"))) @@ -1751,16 +1757,16 @@

    Feedback

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z9f9_qdRSur;BmJBB^7g8aDLwzZ!K+o&OARlYPK1xn4-^{q9?>B3ZO^ajtTta4>O=` zdGnNP^zbY0Y+~65O|`8xNC?#3h>FPn6C{x@JO;L@BW|8We)B~p_sI}P)au5Ekg?7T z01yjM0|1`F&x<*?h5Fn#TrPwL!XG+(_p><#^?rIvcdL(at5!19pnIEBOHjB36&skW z7_~1YilcAg=W92hd{6z~>ZIr58?6>C=d+X;U8O=h(tm9;ge7zt-1VbbA{kqlp=HWbl05d{!ElWTE!18r zr!R}yn^OSJYpG6A_B}6csZ&SNmj) z^eBFuI5sr=AR=V{O6p4wFH4`S-Bn;uw<65-=_wYC*VMm7^ot$^l!9JzBzw=0^?;^G z1b!^xsv*Ukc!Xk^b-r%JGs3J1DG`)K@h^?yq?XTo^QQ=OJ*MA&kz1MaV`B)ICI-YX z@L8h<-znJB5wc+#w<@$sQ%nJiNC%a^(mmCclc0L^6*ArA>8;$BTjGs!w7=bl zsx%|SRj>4o=|pU3vUgdxbv8E8)NJWzLXll@EZ+dnlvs`|T@}sO>cWHOt%lM1fAu^5 zw{)QnsS00#)^QPRxCiH5RJBO}^vE2|g(;ivHu)-(Bod`!keuuY ### Numbers @@ -260,6 +292,27 @@ print('Hello' + ' ' + 'World' * 4) print('Hello' + (' ' + 'World') * 4) ``` +### Boolean + +Booleans represent either `True` or `False`. + +When Python code is run, expressions can be compared and a Boolean answer can be returned: + +```{python boolean1, exercise = TRUE} +print(5 == 5) +print(10 == 2) +print(5 < 4) +``` + +If a value has some sort of content, it is normally evaluated as true: + +```{python boolean2, exercise = TRUE} +bool("Hi") +bool([]) +``` + +Note that the empty `list` in the second expression returned `False`. + ### Data Types and Type Conversion The `type()` function can be used to query the type of a Python object, and any type conversion can be performed by using the appropriate function e.g. `int()` for integer, `str()` for string, and `float()` for a floating point number. @@ -275,6 +328,269 @@ mystr myfloat ``` +## Data Structures + +There are various data structures which are built into Python. Some of the main ones are: + +#### Lists + +- Lists are ordered collections of items. +- They are mutable, meaning you can add, remove, or modify elements. +- Lists can contain elements of different data types. + +```{python list, exercise = TRUE} +my_list = [1, 2, 'apple', 'banana'] +print(my_list) +``` + +#### Tuples + +- Tuples are ordered collections of items, similar to lists. +- Tuples are immutable - once they are created, the elements cannot be changed. +- Tuples can contains elements of different data types. + +```{python tuples, exercise = TRUE} +my_tuple = (1, 2, 'apple', 'banana') +print(my_tuple) +``` + +#### Dictionaries + +- Dictionaries are unordered collections of key-value pairs. +- They are mutable, and each key within a dictionary must be unique. +- Dictionaries are used when you need to associate some data (value) with a specific identifier (key). + + +```{python dictionaries, exercise = TRUE} +my_dict = {'name': 'John', 'age': 30, 'city': 'New York'} +print(my_dict) +``` + +You can then retrieve associate values as such: + +```{python dictionaries2, exercise = TRUE} +my_dict = {'name': 'John', 'age': 30, 'city': 'New York'} + +name_value = my_dict['name'] +print(name_value) +``` + +#### Set + +- Sets are unordered collections of unique items. +- They are mutable, but their elements must be immutable. +- Sets are useful for operations like finding intersections, unions, and differences between collections. + +```{python sets, exercise = TRUE} +my_set = {1, 2, 3, 4, 5} +print(my_set) +``` + +#### Strings + +- Strings are sequences of characters. +- They are immutable, meaning you cannot change the characters in a string once it's created. +- Strings support various operations and methods for text manipulation. + + +```{python stringexample, exercise = TRUE} +my_string = 'Hello, World!' +print(my_string) +``` + +## Functions + +In Python, functions are a block of organised and reusable code which performs a specific task. + +Functions can be called from anywhere within the script, and can be stored anywhere within the script - **however**, it is custom convention to store functions are the top, or near the top, of the script in order to enhance readability and maintenance of the code. + +### Structure + +Functions are made up of the: + +* Defined keyword `def` +* Function name +* `()` parentheses which may include parameters. +* Function body + + +```{python function1, eval = FALSE, echo = TRUE} +def (): + function_body + +``` + +Example: + + +```{python function2, exercise=TRUE} +def greet(): + print("Hello!") +``` + +When you run the above code, you will see that nothing is outputted - this is because the function has not been called. + + +```{python function3, exercise=TRUE} +def greet(): + print("Hello!") + +greet() +``` + +### Parameters and Arguments + +Parameters are variables which are listed inside the parentheses in the function definition. + +They are placeholders are actual values which will be passed into the function when it is called. + +The values passed into a function are called arguments. + + +```{python function4, exercise=TRUE} +def greet(name): + print("Hello, " + name + "!") + +greet("Bob") +``` + +You can also pass through multiple arguments: + + +```{python function5, exercise=TRUE} +def add(a,b,c): + print(a + b + c) + +result = add(1,2,3) +``` + +### Return + +The return statement can optionally be used to return the data back to the caller. + +```{python function6, exercise=TRUE} +def add(a,b,c): + return a + b + c + +result = add(1,2,3) +print(result) +``` + + +## Control Flow & Iteration + +As is the case in most other programming languages, control flow is where decisions are made, and iteration is where processes are repeated. + +### Comparison Operators + +Here are the most commonly used comparison operators used in conditional statements in Python: + +```{r, echo = FALSE} +operators_table <- data.frame( + "Operator" = c("`>` `<`", "`==` `!=`", "`<=` `>=`","`and` `or`"), + "Description" = c("Greater than, Less than", "Equal to, Not Equal to", "Less than or equal to, Greater than or equal to", "And, Or") +) + +knitr::kable(operators_table) +``` + + +### Control Flow - If + +`if` is the most common comparison operator used in Python. + +```{python if1, exercise=TRUE} +if 5 == 5: + print("5 is equal to 5") + +if 5 == 4 or 5 == 5: + print("Either 5 is equal to 4, or 5 is equal to 5") + +if 6 <= 6: + print("6 is less than or equal to 6") +``` + +There are some further statements when using `if` that you should be familiar with: + +- `elif` is short for *else if* and is used for multiple `if` conditions +- `else` is executed if the preceding `if` and `elif` statements are false + + +```{python if2, exercise=TRUE} +def check_if_number_is_1_2_or_3(number): + if number == 1: + print("The number is 1") + elif number == 2: + print("The number is 2") + else: + print("The number is 3") + +check_if_number_is_1_2_or_3(3) +``` + +### For loop + +For loops are used for iteration. + +There are multiple ways to use for loops. + +#### Iterating over a list: + + +```{python forloop1, exercise=TRUE} +list_of_fruits = ["apple","banana","cherry"] + +for fruits in list_of_fruits: + print(fruits) +``` + +#### Using Enumerate: + +You can also use `enumerate` to iterate over a sequence and get the index position of each item also: + +```{python forloop2, exercise=TRUE} +list_of_fruits = ["apple","banana","cherry"] + +for index, fruits in enumerate(list_of_fruits): + print(index,fruits) +``` + + +#### Iterating over a string: + + +```{python forloop3, exercise=TRUE} +for char in "Python": + print(char) +``` + +#### Iterating over a range: + +`range` in Python determines the position of the value, rather than the value itself. + +Remember that positions in Python begin at position `0`. + +```{python forloop4, exercise=TRUE} + +for i in range(5): + print(i) + +``` + +#### Nested Loops + +```{python forloop5, exercise=TRUE} + +adjective = ["red", "big", "tasty"] +fruits = ["apple", "banana", "cherry"] + +for x in adjective: + for y in fruits: + print(x, y) + +``` + + ## Importing and Exporting Datasets The Python language can be used for data analysis. The first step in performing analysis is to access your dataframe (i.e. your dataset). This section will introduce you how to import and export datasets. @@ -379,6 +695,7 @@ hello_world <- "Hello World" print(hello_world) ``` + ## Explore ### Mean/Median & Summary diff --git a/intro.html b/intro.html index 2a65a14..6619660 100644 --- a/intro.html +++ b/intro.html @@ -10,7 +10,7 @@ - + @@ -145,6 +145,33 @@

      What is Python?

      widespread use in many application domains. Python is open source and free to use, and available for all major operating systems.

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zflRg2a;ip~+*qgyS4R4}tJ40w+Ql2!!JPQM zvcIUaeme3p6)c#7#!zReSfWz7$`id|ZmW&n8rLPn{>+O%ogFs=Kzf-q&;Q^xeqPT{ zH~b4{%os1+%~UFZaXpk!V`iDUySw+nKQU$MDQhN7Tb3~KZt#l>Rz z$$UUM;`xe?h=c^VJ={FZSCKa2K0~VA)f7pX0ql_mFnWq&rF88SNQi&5$rG<8`i5%s zQ8H^KT%SFO97qu@yL2-3I(JxQOm!_DN0PUD|9rk>Htu_{aasX6)D~&;a@&eomX%+a~_*aW9z@y5SHJw41e=p(w?UeHx0EZ@f>uUOU1N!Hk z`=89Y#%&wNXPv|ul{f{y$Q*i%^aGd`8yZ--v9!4sg`|NZb1u55J zp>Vge<2XKu7bk%RwYyd}QD|sr=VWC?1yCE!*e6**C2YWP`ITqZ+sqe%|KDZ)+HZ&- z*xa9|$MD0-6Xeu;KMz=Nq^73c$5#Lf+~c_f1n$_`*)7>s*j?6nf`6spUn%(ip%j?Ax??Cl Xy&h2tdg0n}4)}R0CMOEa+41;4ZS46| literal 0 HcmV?d00001 diff --git a/intro.Rmd b/intro.Rmd index 5753120..390f4d9 100755 --- a/intro.Rmd +++ b/intro.Rmd @@ -3,12 +3,11 @@ title: "Intro to Python" output: learnr::tutorial: css: "css/style.css" - progressive: false + progressive: true allow_skip: true runtime: shiny_prerendered --- - ```{r setup, include=FALSE} # Author: Tina Fu # Original Date: February 2024 @@ -17,8 +16,12 @@ runtime: shiny_prerendered # See here for learnr package documentation: https://rstudio.github.io/learnr/ # Include packages here that are required throughout the training + +#install.packages("learnr") library(learnr) # Required to build the Shiny app -# library(gradethis) # Required for specific code checking and specific feedback +#install.packages("gradethis") +library(gradethis) # Required for specific code checking and specific feedback +#install.packages("reticulate") library(reticulate) knitr::opts_chunk$set(echo = FALSE) @@ -187,12 +190,41 @@ knitr::include_graphics("images/markdown-example2.PNG") Python library is a collection of functions and methods that allows you to perform lots of actions without writing your own code. For example, “pandas” is a Python library for data manipulation and analysis, which is used a lot in this training guidance. +## Foundations -### VS Code +This section will walk you through some of the foundational knowledge of Python, including structure, style, and key data types. -## Foundations +### Structure + +**Indentation** - where indentation in other programming languages are included as a style preference, indentation in Python is extremely important, as it indicates what blocks of code should be run. -This section will walk you through the key data types and structures necessary to hit the ground running and start using Python productively. +Without proper indentations, your code will not run. Try to run the following code with improper indentation: + +```{python indentation1, exercise=TRUE} +if 1 < 10: +print("One is less than ten.") +``` + +Note the syntax error which was shown: + +Now run the code with the proper indentation: + +```{python indentation2, exercise=TRUE} +if 1 < 10: + print("One is less than ten.") +``` + +Indentation not only affords proper functionality, but it also enhances readability and comprehension for yourself, and future co-developers. + +### Style + +**Naming** - variables and filenames should have meaningful names in *`snake_case`* format, preferring all lower case. + +

    +
    +

    How does Python run?

    +

    Traditionally, when a Python script is run, the entire script is +interpreted and run from the top down.

    +
    a = 15
+b = 10
+c = a + b 
+print(c)
    +

    When the above Python script is run, the entire script will be run +from the top down, resulting in an output of 25.

    +

    There are a number of advantages to having Python scripts run this +way. To name a few:

    +
      +
    • The script has a sequential flow
      • +
    • It is easier to debug
      • +
    • It is clearer and more readable
      • +
    +

    However, there are a number of ways to run individual lines of code +within a Python script, should that be preferred. In this course, we +will touch upon Jupyter Notebook in the IDE +section, where this can be accomplished.

    +
    +
    +
    +

    IDE

    +

    Within this section, we will cover two IDEs which can be used to +create and run Python scripts.

    Jupyter Notebook

    There are several environments where you can run Python codes, such @@ -263,9 +290,45 @@

    Python Library

    Foundations

    -

    This section will walk you through the key data types and structures -necessary to hit the ground running and start using Python -productively.

    +

    This section will walk you through some of the foundational knowledge +of Python, including structure, style, and key data types.

    +
    +

    Structure

    +

    Indentation - where indentation in other programming +languages are included as a style preference, indentation in Python is +extremely important, as it indicates what blocks of code should be +run.

    +

    Without proper indentations, your code will not run. Try to run the +following code with improper indentation:

    +
    +
    if 1 < 10:
+print("One is less than ten.")
    + +
    +

    Note the syntax error which was shown:

    +

    Now run the code with the proper indentation:

    +
    +
    if 1 < 10:
+  print("One is less than ten.")
    + +
    +

    Indentation not only affords proper functionality, but it also +enhances readability and comprehension for yourself, and future +co-developers.

    +
    +
    +

    Style

    +

    Naming - variables and filenames should have +meaningful names in snake_case format, preferring +all lower case.

    +
    +

    +
    +

    Numbers

    There are 3 main types of numbers that can be declared in Python.

    @@ -322,15 +385,11 @@

    Strings

    -
    def greet(name):
-    print(f"Hello, {name}!")
+
    multiline = '''White space is
+preserved
+in multiline strings.'''
 
-def main():
-    user_name = input("Enter your name: ")
-    greet(user_name)
-
-if __name__ == "__main__":
-    main()
    
+multiline

    The print() function interprets these escape characters @@ -365,6 +424,32 @@

    Strings

    +
    +

    Boolean

    +

    Booleans represent either True or +False.

    +

    When Python code is run, expressions can be compared and a Boolean +answer can be returned:

    +
    +
    print(5 == 5)
+print(10 == 2)
+print(5 < 4)
    + +
    +

    If a value has some sort of content, it is normally evaluated as +true:

    +
    +
    bool("Hi")
+bool([])
    + +
    +

    Note that the empty list in the second expression +returned False.

    +

    Data Types and Type Conversion

    The type() function can be used to query the type of a @@ -387,6 +472,332 @@

    Data Types and Type Conversion

    +
    +

    Data Structures

    +

    There are various data structures which are built into Python. Some +of the main ones are:

    +
    +

    Lists

    +
      +
    • Lists are ordered collections of items.
      • +
    • They are mutable, meaning you can add, remove, or modify +elements.
      • +
    • Lists can contain elements of different data types.
      • +
    +
    +
    my_list = [1, 2, 'apple', 'banana']
+print(my_list)
    + +
    +
    +
    +

    Tuples

    +
      +
    • Tuples are ordered collections of items, similar to lists.
      • +
    • Tuples are immutable - once they are created, the elements cannot be +changed.
      • +
    • Tuples can contains elements of different data types.
      • +
    +
    +
    my_tuple = (1, 2, 'apple', 'banana')
+print(my_tuple)
    + +
    +
    +
    +

    Dictionaries

    +
      +
    • Dictionaries are unordered collections of key-value pairs.
      • +
    • They are mutable, and each key within a dictionary must be +unique.
      • +
    • Dictionaries are used when you need to associate some data (value) +with a specific identifier (key).
      • +
    +
    +
    my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}
+print(my_dict)
    + +
    +

    You can then retrieve associate values as such:

    +
    +
    my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}
+
+name_value = my_dict['name']
+print(name_value)
    + +
    +
    +
    +

    Set

    +
      +
    • Sets are unordered collections of unique items.
      • +
    • They are mutable, but their elements must be immutable.
      • +
    • Sets are useful for operations like finding intersections, unions, +and differences between collections.
      • +
    +
    +
    my_set = {1, 2, 3, 4, 5}
+print(my_set)
    + +
    +
    +
    +

    Strings

    +
      +
    • Strings are sequences of characters.
      • +
    • They are immutable, meaning you cannot change the characters in a +string once it’s created.
      • +
    • Strings support various operations and methods for text +manipulation.
      • +
    +
    +
    my_string = 'Hello, World!'
+print(my_string)
    + +
    +
    +
    +
    +

    Functions

    +

    In Python, functions are a block of organised and reusable code which +performs a specific task.

    +

    Functions can be called from anywhere within the script, and can be +stored anywhere within the script - however, it is +custom convention to store functions are the top, or near the top, of +the script in order to enhance readability and maintenance of the +code.

    +
    +

    Structure

    +

    Functions are made up of the:

    +
      +
    • Defined keyword def
      • +
    • Function name
      • +
    • () parentheses which may include parameters.
      • +
    • Function body
      • +
    +
    def <function_name>():
+  function_body
    +

    Example:

    +
    +
    def greet():
+  print("Hello!")
    + +
    +

    When you run the above code, you will see that nothing is outputted - +this is because the function has not been called.

    +
    +
    def greet():
+  print("Hello!")
+
+greet()
    + +
    +
    +
    +

    Parameters and Arguments

    +

    Parameters are variables which are listed inside the parentheses in +the function definition.

    +

    They are placeholders are actual values which will be passed into the +function when it is called.

    +

    The values passed into a function are called arguments.

    +
    +
    def greet(name):
+  print("Hello, " + name + "!")
+  
+greet("Bob")
    + +
    +

    You can also pass through multiple arguments:

    +
    +
    def add(a,b,c):
+  print(a + b + c)
+  
+result = add(1,2,3)
    + +
    +
    +
    +

    Return

    +

    The return statement can optionally be used to return the data back +to the caller.

    +
    +
    def add(a,b,c):
+  return a + b + c
+  
+result = add(1,2,3)
+print(result)
    + +
    +
    +
    +
    +

    Control Flow & Iteration

    +

    As is the case in most other programming languages, control flow is +where decisions are made, and iteration is where processes are +repeated.

    +
    +

    Comparison Operators

    +

    Here are the most commonly used comparison operators used in +conditional statements in Python:

    + + + + + + + + + + + + + + + + + + + + + + + + + +
    OperatorDescription
    > <Greater than, Less than
    == !=Equal to, Not Equal to
    <= >=Less than or equal to, Greater than or equal to
    and orAnd, Or
    +
    +
    +

    Control Flow - If

    +

    if is the most common comparison operator used in +Python.

    +
    +
    if 5 == 5:
+  print("5 is equal to 5")
+
+if 5 == 4 or 5 == 5:
+  print("Either 5 is equal to 4, or 5 is equal to 5")
+
+if 6 <= 6:
+  print("6 is less than or equal to 6")
    + +
    +

    There are some further statements when using if that you +should be familiar with:

    +
      +
    • elif is short for else if and is used for +multiple if conditions
      • +
    • else is executed if the preceding if and +elif statements are false
      • +
    +
    +
    def check_if_number_is_1_2_or_3(number):
+  if number == 1:
+    print("The number is 1")
+  elif number == 2:
+    print("The number is 2")
+  else:
+    print("The number is 3")
+
+check_if_number_is_1_2_or_3(3)
    + +
    +
    +
    +

    For loop

    +

    For loops are used for iteration.

    +

    There are multiple ways to use for loops.

    +
    +

    Iterating over a list:

    +
    +
    list_of_fruits = ["apple","banana","cherry"]
+
+for fruits in list_of_fruits:
+  print(fruits)
    + +
    +
    +
    +

    Using Enumerate:

    +

    You can also use enumerate to iterate over a sequence +and get the index position of each item also:

    +
    +
    list_of_fruits = ["apple","banana","cherry"]
+
+for index, fruits in enumerate(list_of_fruits):
+  print(index,fruits)
    + +
    +
    +
    +

    Iterating over a string:

    +
    +
    for char in "Python":
+    print(char)
    + +
    +
    +
    +

    Iterating over a range:

    +

    range in Python determines the position of the value, +rather than the value itself.

    +

    Remember that positions in Python begin at position +0.

    +
    +
    for i in range(5):
+    print(i)
    + +
    +
    +
    +

    Nested Loops

    +
    +
    adjective = ["red", "big", "tasty"]
+fruits = ["apple", "banana", "cherry"]
+
+for x in adjective:
+    for y in fruits:
+        print(x, y)
    + +
    +
    +
    +

    Importing and Exporting Datasets

    @@ -512,6 +923,10 @@

    Mean/Median & Summary

  • describe() returns all summary statistics based on a given array.

    • +

    For example, +<df_name>["<col_name>"].median() will generate +the median value of the values within the stated +column.

    In the exercise below, you have the borders data-set loaded as borders_data. See if you can get the mean value for LengthOfStay, store it in a variable, and print that @@ -1279,8 +1694,12 @@

    Feedback

    # See here for learnr package documentation: https://rstudio.github.io/learnr/ # Include packages here that are required throughout the training + +#install.packages("learnr") library(learnr) # Required to build the Shiny app -# library(gradethis) # Required for specific code checking and specific feedback +#install.packages("gradethis") +library(gradethis) # Required for specific code checking and specific feedback +#install.packages("reticulate") library(reticulate) knitr::opts_chunk$set(echo = FALSE) @@ -1311,25 +1730,26 @@

    Feedback

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -1561,12 +3040,13 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, - chunks = list(list(label = "csv-read-example", code = "# Import pandas library\nimport pandas as pd \n# Read in the dataset\nborders = pd.read_csv(\"data/borders_inc_age.csv\") \n# Check the first few rows of the dataset. Default is 5 rows. \nborders.head() ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = NULL, chunks = list( + list(label = "csv-read-example", code = "# Import pandas library\nimport pandas as pd \n# Read in the dataset\nborders = pd.read_csv(\"data/borders_inc_age.csv\") \n# Check the first few rows of the dataset. Default is 5 rows. \nborders.head() ", opts = list(label = "\"csv-read-example\"", exercise = "TRUE", engine = "\"python\""), engine = "python")), code_check = NULL, error_check = NULL, check = NULL, solution = NULL, tests = NULL, @@ -1610,11 +3090,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -1663,11 +3144,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -1715,11 +3197,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -1757,16 +3240,16 @@

    Feedback

    @@ -1795,12 +3278,13 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, - chunks = list(list(label = "example-code-q", code = "# Hello World example\nhello_world <- \"Hello World\"\n\nprint(hello_world)", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = NULL, chunks = list( + list(label = "example-code-q", code = "# Hello World example\nhello_world <- \"Hello World\"\n\nprint(hello_world)", opts = list(label = "\"example-code-q\"", exercise = "TRUE"), engine = "r")), code_check = NULL, error_check = NULL, check = NULL, solution = NULL, tests = NULL, options = list( @@ -1841,11 +3325,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -1896,11 +3381,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -1951,11 +3437,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2006,11 +3493,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2060,11 +3548,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2115,11 +3604,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2169,11 +3659,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2223,11 +3714,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2277,11 +3769,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2331,11 +3824,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2382,11 +3876,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2437,11 +3932,12 @@

    Feedback

    "# Original Date: February 2024", "# Version of R: 4.3.2", "", "# See here for learnr package documentation: https://rstudio.github.io/learnr/", "", "# Include packages here that are required throughout the training", -"library(learnr) # Required to build the Shiny app", -"# library(gradethis) # Required for specific code checking and specific feedback", -"library(reticulate)", "", "knitr::opts_chunk$set(echo = FALSE)", -"", "tutorial_options(", " exercise.checker = gradethis::grade_learnr", -")"), chunk_opts = list(label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", +"", "#install.packages(\"learnr\")", "library(learnr) # Required to build the Shiny app", +"#install.packages(\"gradethis\")", "library(gradethis) # Required for specific code checking and specific feedback", +"#install.packages(\"reticulate\")", "library(reticulate)", "", +"knitr::opts_chunk$set(echo = FALSE)", "", "tutorial_options(", +" exercise.checker = gradethis::grade_learnr", ")"), chunk_opts = list( + label = "setup", include = FALSE)), setup = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", chunks = list(list(label = "pandas-setup", code = "# Import pandas library\nimport pandas as pd \n\nborders2 = pd.read_csv(\"data/borders_inc_age.csv\", usecols = ['URI', 'HospitalCode', 'Specialty']) ", opts = list(label = "\"pandas-setup\"", include = "FALSE", eval = "FALSE", engine = "\"python\""), engine = "python"), @@ -2483,7 +3979,7 @@

    Feedback

    From 04d0992c429d022605d7fc8d9820577f1c22562f Mon Sep 17 00:00:00 2001 From: vly12 Date: Mon, 29 Apr 2024 15:16:44 +0100 Subject: [PATCH 13/15] Minor edits. --- intro.Rmd | 4 +--- intro.html | 10 ++++------ 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/intro.Rmd b/intro.Rmd index 390f4d9..6321f24 100755 --- a/intro.Rmd +++ b/intro.Rmd @@ -32,7 +32,7 @@ tutorial_options( ``` -```{python pandas-setup, include = FALSE, eval = FALSE} +```{python pandas-setup, include = FALSE, eval = TRUE} # Import pandas library import pandas as pd @@ -86,8 +86,6 @@ However, there are a number of ways to run individual lines of code within a Pyt ## IDE -Within this section, we will cover two IDEs which can be used to create and run Python scripts. - ### Jupyter Notebook There are several environments where you can run Python codes, such as "JupyterLab", "Jupyter Notebook". In this training course, we will use “Jupyter Notebook”. diff --git a/intro.html b/intro.html index 6619660..1c5fe97 100644 --- a/intro.html +++ b/intro.html @@ -170,8 +170,6 @@

    How does Python run?

    IDE

    -

    Within this section, we will cover two IDEs which can be used to -create and run Python scripts.

    Jupyter Notebook

    There are several environments where you can run Python codes, such @@ -3240,16 +3238,16 @@

    Feedback

    From fce23daeaa0fb15f154bb8563d2df2186acd604a Mon Sep 17 00:00:00 2001 From: vly12 Date: Mon, 29 Apr 2024 15:33:56 +0100 Subject: [PATCH 14/15] small updates --- intro.Rmd | 2 +- intro.html | 8 ++++---- python-training.Rproj | 26 +++++++++++++------------- 3 files changed, 18 insertions(+), 18 deletions(-) diff --git a/intro.Rmd b/intro.Rmd index 6321f24..6685738 100755 --- a/intro.Rmd +++ b/intro.Rmd @@ -32,7 +32,7 @@ tutorial_options( ``` -```{python pandas-setup, include = FALSE, eval = TRUE} +```{python pandas-setup, include = FALSE, eval = FALSE} # Import pandas library import pandas as pd diff --git a/intro.html b/intro.html index 1c5fe97..acaa734 100644 --- a/intro.html +++ b/intro.html @@ -3238,16 +3238,16 @@

    Feedback

    diff --git a/python-training.Rproj b/python-training.Rproj index 8e3c2eb..3af27f6 100755 --- a/python-training.Rproj +++ b/python-training.Rproj @@ -1,13 +1,13 @@ -Version: 1.0 - -RestoreWorkspace: Default -SaveWorkspace: Default -AlwaysSaveHistory: Default - -EnableCodeIndexing: Yes -UseSpacesForTab: Yes -NumSpacesForTab: 2 -Encoding: UTF-8 - -RnwWeave: Sweave -LaTeX: pdfLaTeX +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX From 745e38a0f4b18bfe6a207b4b980436eae1e927b0 Mon Sep 17 00:00:00 2001 From: vly12 Date: Mon, 29 Apr 2024 15:53:16 +0100 Subject: [PATCH 15/15] deleted html --- .gitignore | 4 +- intro.html | 4052 ---------------------------------------------------- 2 files changed, 3 insertions(+), 4053 deletions(-) delete mode 100644 intro.html diff --git a/.gitignore b/.gitignore index f99738a..d1902b1 100644 --- a/.gitignore +++ b/.gitignore @@ -5,4 +5,6 @@ rsconnect/** -.DS_Store \ No newline at end of file +.DS_Store + +*.html \ No newline at end of file diff --git a/intro.html b/intro.html deleted file mode 100644 index acaa734..0000000 --- a/intro.html +++ /dev/null @@ -1,4052 +0,0 @@ - - - - - - - - - - - - - - - - -Intro to Python - - - - - - - - - - - - - - - - - - - - - - -    Skip to Tutorial Content - - - -
    -
    - -
    - -

    -
    -

    Introduction

    -

    Welcome to an introduction to python. This course is designed as a -self-led introduction to R Markdown for anyone in Public Health -Scotland.

    -
    -

    -Course Info -

    -
      -
    • -This course is built to flow through sections and build on previous -knowledge. If you’re comfortable with a particular section, you can skip -it. -
      • -
    • -Most sections have multiple parts to them. Navigate the course by using -the buttons at the bottom of the screen to Continue or go to the Next -Topic. -
      • -
    • -The course will also show progress through sections, a green tick will -appear on sections you’ve completed, and it will remember your place if -you decide to close your browser and come back later. -
      • -
    -
    -


    -
    -

    What is Python?

    -

    Python is a powerful general purpose programming language with -widespread use in many application domains. Python is open source and -free to use, and available for all major operating systems.

    -
    -
    -

    How does Python run?

    -

    Traditionally, when a Python script is run, the entire script is -interpreted and run from the top down.

    -
    a = 15
-b = 10
-c = a + b 
-print(c)
    -

    When the above Python script is run, the entire script will be run -from the top down, resulting in an output of 25.

    -

    There are a number of advantages to having Python scripts run this -way. To name a few:

    -
      -
    • The script has a sequential flow
      • -
    • It is easier to debug
      • -
    • It is clearer and more readable
      • -
    -

    However, there are a number of ways to run individual lines of code -within a Python script, should that be preferred. In this course, we -will touch upon Jupyter Notebook in the IDE -section, where this can be accomplished.

    -
    -
    -
    -

    IDE

    -
    -

    Jupyter Notebook

    -

    There are several environments where you can run Python codes, such -as “JupyterLab”, “Jupyter Notebook”. In this training course, we will -use “Jupyter Notebook”.

    -

    Jupyter Notebook is designed for the easy integration of text and -Python programming. It provides a more interactive workflow for Python -programming, analysis and reporting. Some of its key features are:

    -
      -
    • Code completion
      • -
    • Syntax highlighting
      • -
    • Code refactoring (find and replace)
      • -
    • Integrated documentation viewer
      • -
    • Ability to combine text, equations and images as well as code in a -single document
      • -
    • All outputs of the executed code are saved and embedded in the -notebook
      • -
    • Cell based execution and editing of code/text segments
      • -
    -
    -

    Open a New Jupyter Notebook

    -

    We can access “Jupyter Notebook” on Posit Workbench. After -signing in, click on New Session and a diaglog box will pop up. Click on -Editor and select “Jupyter Notebook” from the drop down list, and then -Start Session.

    -

    -


    -

    You will see the interface looks like this. There are three main tabs -in Jupyter on start-up:

    -
      -
    • Files Your file directory
      • -
    • Running Lists all of the notebooks currently -running
      • -
    • Clusters For using IPython in parallel with your -cluster (beyond the scope of this training guidance)
      • -
    -

    -


    -

    To open a new Jupyter Notebook, click the “New” drop down menu on the -Files tab and select “Python 3.10.2” under the notebooks heading. This -will open a blank Notebook with an IPython console running underneath -it.

    -

    -

    -


    -

    The IPython console is used to input and execute Python code -interactively. Outputs, errors and warning messages are directly shown -in the same window. A command that has been entered into the console is -executed by

    -
      -

    • Pressing Shift + Enter to execute all the codes in the -current active cell and advance the cursor to the next cell. Or press -the “run cell, select below” button on the toolbar.

      • -

    • Pressing Ctrl + Enter to execute all the codes in the -current active cell. If you want to move to the next cell below, just -simply click on the “insert cell below” button on the toolbar.

      • -
    -
    -
    -

    Command and Edit Modes

    -

    Jupyter notebook is a modal editor which means that the keyboard does -different things depending on which mode the Notebook is in. There are -two modes: edit mode and command -mode.

    -
      -
    • Edit mode - it is indicated by a green cell border -and left sidebar, and a prompt showing in the editor area:
      • -
    -

    -

    When a cell is in edit mode, you can type things such as Python codes -into the cell, like a normal text editor. Enter edit mode by pressing -Enter or using the mouse to click on a cell’s editor area.

    -
      -
    • Command mode - Once you click somewhere else -outside the cell or press “esc” on keyboard, the cell turns into Command -mode. Command mode is indicated by a grey cell border and a blue -sidebar:
      • -
    -

    -

    When you are in command mode, you are able to edit the notebook as a -whole, but not type into individual cells. Most importantly, in command -mode, the keyboard is mapped to a set of shortcuts that let you perform -notebook and cell actions efficiently. For example, if you are in -command mode and you press C and V, you will copy and paste the current -cell.

    -

    A full list of useful shortcuts is available by going to “Help > -Keyboard Shortcuts” (You can also access by pressing H in command -mode).

    -
    -
    -

    Markdown Text Cells

    -

    Markdown text cells support plain text, Markdown and HTML. It will be -useful to create headings, text instructions etc using markdown to -organise the notebook like a written document. A cell can be changed -from code mode to markdown mode by going to the top menu bar “Cell > -Cell Type > Markdown”. Or select “Markdown” from the dropdown -list:

    -

    -

    Or press M while in Command Mode and highlighting the cell.

    -

    Here is an example of typing some text in a markdown cell. You can -use hash key “#” to indicate the size of heading, followed by a space -and the text.

    -

    -

    Then press Shift + Enter to finish.

    -

    -
    -
    -

    Python Library

    -

    Python library is a collection of functions and methods that allows -you to perform lots of actions without writing your own code. For -example, “pandas” is a Python library for data manipulation and -analysis, which is used a lot in this training guidance.

    -
    -
    -
    -
    -

    Foundations

    -

    This section will walk you through some of the foundational knowledge -of Python, including structure, style, and key data types.

    -
    -

    Structure

    -

    Indentation - where indentation in other programming -languages are included as a style preference, indentation in Python is -extremely important, as it indicates what blocks of code should be -run.

    -

    Without proper indentations, your code will not run. Try to run the -following code with improper indentation:

    -
    -
    if 1 < 10:
-print("One is less than ten.")
    - -
    -

    Note the syntax error which was shown:

    -

    Now run the code with the proper indentation:

    -
    -
    if 1 < 10:
-  print("One is less than ten.")
    - -
    -

    Indentation not only affords proper functionality, but it also -enhances readability and comprehension for yourself, and future -co-developers.

    -
    -
    -

    Style

    -

    Naming - variables and filenames should have -meaningful names in snake_case format, preferring -all lower case.

    -
    -

    -
    -
    -
    -

    Numbers

    -

    There are 3 main types of numbers that can be declared in Python.

    -
    age = 27 # Integers
-height = 1.76 # Floating point numbers
-k = 6.626e-32 # Using scientific notation
-var1 = 2 + 5.2j # Complex numbers
    -
    -
    -

    Arithmetic Operators

    -

    The following are examples of all the arithmetic operators available -in Python.

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    PrecedenceOperatorDescription
    1+ -Addition, Subtraction
    2* ** /Multiplication, Power, Division
    3//Floor Division (round down after division)
    4%Modulus (remainder after division)
    -
    -
    -

    Strings

    -

    Strings in Python are an ordered sequence of characters, and can be -declared with either single or double quotes.

    -
    name = 'John'
-surname = 'Doe'
    -

    Multiline strings can be declared with triple single and triple -double quotes, but note that whitespace characters are recorded in the -string e.g. for tabs and for newlines.

    -
    -
    multiline = '''White space is
-preserved
-in multiline strings.'''
-
-multiline
    - -
    -

    The print() function interprets these escape characters -as expected.

    -
    -
    print(multiline)
    - -
    -

    Different string quoting styles can be nested as only the outer one -is used. You can also use a backslash to escape quote characters within -the string if you need to display them.

    -
    -
    nested_quotes = 'It\'s sometimes "necessary" to escape things'
-nested_quotes
-print(nested_quotes)
    - -
    -

    For strings, the plus + sign will concatenate two -strings into one, and the asterisk * will repeat a string a -set number of times. Have a look and click ‘Run Code’ below to see the -output.

    -
    -
    print('Hello' + ' ' + 'World')
-print('Hello' + ' ' + 'World' * 4)
-print('Hello' + (' ' + 'World') * 4)
    - -
    -
    -
    -

    Boolean

    -

    Booleans represent either True or -False.

    -

    When Python code is run, expressions can be compared and a Boolean -answer can be returned:

    -
    -
    print(5 == 5)
-print(10 == 2)
-print(5 < 4)
    - -
    -

    If a value has some sort of content, it is normally evaluated as -true:

    -
    -
    bool("Hi")
-bool([])
    - -
    -

    Note that the empty list in the second expression -returned False.

    -
    -
    -

    Data Types and Type Conversion

    -

    The type() function can be used to query the type of a -Python object, and any type conversion can be performed by using the -appropriate function e.g. int() for integer, -str() for string, and float() for a floating -point number.

    -
    -
    myint = 12345
-type(myint) # Check the data type of myint
-
-mystr = str(myint) # Convert myint to a string
-myfloat = float(myint) # Convert myint to a floating point number
-
-mystr
-myfloat
    - -
    -
    -
    -
    -

    Data Structures

    -

    There are various data structures which are built into Python. Some -of the main ones are:

    -
    -

    Lists

    -
      -
    • Lists are ordered collections of items.
      • -
    • They are mutable, meaning you can add, remove, or modify -elements.
      • -
    • Lists can contain elements of different data types.
      • -
    -
    -
    my_list = [1, 2, 'apple', 'banana']
-print(my_list)
    - -
    -
    -
    -

    Tuples

    -
      -
    • Tuples are ordered collections of items, similar to lists.
      • -
    • Tuples are immutable - once they are created, the elements cannot be -changed.
      • -
    • Tuples can contains elements of different data types.
      • -
    -
    -
    my_tuple = (1, 2, 'apple', 'banana')
-print(my_tuple)
    - -
    -
    -
    -

    Dictionaries

    -
      -
    • Dictionaries are unordered collections of key-value pairs.
      • -
    • They are mutable, and each key within a dictionary must be -unique.
      • -
    • Dictionaries are used when you need to associate some data (value) -with a specific identifier (key).
      • -
    -
    -
    my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}
-print(my_dict)
    - -
    -

    You can then retrieve associate values as such:

    -
    -
    my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}
-
-name_value = my_dict['name']
-print(name_value)
    - -
    -
    -
    -

    Set

    -
      -
    • Sets are unordered collections of unique items.
      • -
    • They are mutable, but their elements must be immutable.
      • -
    • Sets are useful for operations like finding intersections, unions, -and differences between collections.
      • -
    -
    -
    my_set = {1, 2, 3, 4, 5}
-print(my_set)
    - -
    -
    -
    -

    Strings

    -
      -
    • Strings are sequences of characters.
      • -
    • They are immutable, meaning you cannot change the characters in a -string once it’s created.
      • -
    • Strings support various operations and methods for text -manipulation.
      • -
    -
    -
    my_string = 'Hello, World!'
-print(my_string)
    - -
    -
    -
    -
    -

    Functions

    -

    In Python, functions are a block of organised and reusable code which -performs a specific task.

    -

    Functions can be called from anywhere within the script, and can be -stored anywhere within the script - however, it is -custom convention to store functions are the top, or near the top, of -the script in order to enhance readability and maintenance of the -code.

    -
    -

    Structure

    -

    Functions are made up of the:

    -
      -
    • Defined keyword def
      • -
    • Function name
      • -
    • () parentheses which may include parameters.
      • -
    • Function body
      • -
    -
    def <function_name>():
-  function_body
    -

    Example:

    -
    -
    def greet():
-  print("Hello!")
    - -
    -

    When you run the above code, you will see that nothing is outputted - -this is because the function has not been called.

    -
    -
    def greet():
-  print("Hello!")
-
-greet()
    - -
    -
    -
    -

    Parameters and Arguments

    -

    Parameters are variables which are listed inside the parentheses in -the function definition.

    -

    They are placeholders are actual values which will be passed into the -function when it is called.

    -

    The values passed into a function are called arguments.

    -
    -
    def greet(name):
-  print("Hello, " + name + "!")
-  
-greet("Bob")
    - -
    -

    You can also pass through multiple arguments:

    -
    -
    def add(a,b,c):
-  print(a + b + c)
-  
-result = add(1,2,3)
    - -
    -
    -
    -

    Return

    -

    The return statement can optionally be used to return the data back -to the caller.

    -
    -
    def add(a,b,c):
-  return a + b + c
-  
-result = add(1,2,3)
-print(result)
    - -
    -
    -
    -
    -

    Control Flow & Iteration

    -

    As is the case in most other programming languages, control flow is -where decisions are made, and iteration is where processes are -repeated.

    -
    -

    Comparison Operators

    -

    Here are the most commonly used comparison operators used in -conditional statements in Python:

    - - - - - - - - - - - - - - - - - - - - - - - - - -
    OperatorDescription
    > <Greater than, Less than
    == !=Equal to, Not Equal to
    <= >=Less than or equal to, Greater than or equal to
    and orAnd, Or
    -
    -
    -

    Control Flow - If

    -

    if is the most common comparison operator used in -Python.

    -
    -
    if 5 == 5:
-  print("5 is equal to 5")
-
-if 5 == 4 or 5 == 5:
-  print("Either 5 is equal to 4, or 5 is equal to 5")
-
-if 6 <= 6:
-  print("6 is less than or equal to 6")
    - -
    -

    There are some further statements when using if that you -should be familiar with:

    -
      -
    • elif is short for else if and is used for -multiple if conditions
      • -
    • else is executed if the preceding if and -elif statements are false
      • -
    -
    -
    def check_if_number_is_1_2_or_3(number):
-  if number == 1:
-    print("The number is 1")
-  elif number == 2:
-    print("The number is 2")
-  else:
-    print("The number is 3")
-
-check_if_number_is_1_2_or_3(3)
    - -
    -
    -
    -

    For loop

    -

    For loops are used for iteration.

    -

    There are multiple ways to use for loops.

    -
    -

    Iterating over a list:

    -
    -
    list_of_fruits = ["apple","banana","cherry"]
-
-for fruits in list_of_fruits:
-  print(fruits)
    - -
    -
    -
    -

    Using Enumerate:

    -

    You can also use enumerate to iterate over a sequence -and get the index position of each item also:

    -
    -
    list_of_fruits = ["apple","banana","cherry"]
-
-for index, fruits in enumerate(list_of_fruits):
-  print(index,fruits)
    - -
    -
    -
    -

    Iterating over a string:

    -
    -
    for char in "Python":
-    print(char)
    - -
    -
    -
    -

    Iterating over a range:

    -

    range in Python determines the position of the value, -rather than the value itself.

    -

    Remember that positions in Python begin at position -0.

    -
    -
    for i in range(5):
-    print(i)
    - -
    -
    -
    -

    Nested Loops

    -
    -
    adjective = ["red", "big", "tasty"]
-fruits = ["apple", "banana", "cherry"]
-
-for x in adjective:
-    for y in fruits:
-        print(x, y)
    - -
    -
    -
    -
    -
    -

    Importing and Exporting Datasets

    -

    The Python language can be used for data analysis. The first step in -performing analysis is to access your dataframe (i.e. your dataset). -This section will introduce you how to import and export datasets.

    -
    -

    Read and Save .csv Files

    -

    Various commonly used file formats can be read using Python, such as -.csv and .xls files. You can import these files by using the “pandas” -library. The general code is

    -
    dataset_name = pd.read_csv("filename.csv")
    -

    There are three pieces to this code:

    -
      -
    • to the right of the = sign is the pandas import code: -pd.read_csv("filename.csv")
      • -
    • to the left of the = sign is the name we’ve given to -the dataset: dataset_name
      • -
    • the = sign tells Python to connect the name -dataset_name to the imported dataset
      • -
    -

    This is an example of a more general concept in Python called -variable assignment. A variable in -Python is a name for referring to an object, just like -dataset_name refers to the imported dataset. The object in -question is called the value of the variable.

    -

    To assign a value to a variable, we use the = sign as -above:

    -
    variable = value
    -

    Also remember that Python is a case-sensitive -language.

    -

    Here is a real example for importing a dataset “borders_inc_age.csv”. -Have a look and click ‘Run Code’ below to see the output.

    -
    -
    # Import pandas library
-import pandas as pd 
-# Read in the dataset
-borders = pd.read_csv("data/borders_inc_age.csv") 
-# Check the first few rows of the dataset. Default is 5 rows. 
-borders.head()
    - -
    -

    If you make any changes to borders and would like to save it as a new -.csv file, use the following command:

    -
    borders.to_csv("file path and name")
    -
    -
    -

    Read Specific Columns

    -

    It is possible to omit certain columns from a dataframe when -importing a file by using usecols command:

    -
    -
    # Read in the dataset with specific columns
-borders2 = pd.read_csv("data/borders_inc_age.csv", usecols = ['URI', 'HospitalCode', 'Specialty']) 
-
-# Check the first few rows of the dataset. Default is 5 rows. 
-borders2.head()
    - -
    -

    It is also possible to rearrange the columns within an imported -dataframe. The columns in the example above can be rearranged using the -following code:

    -
    -
    # Rearrange the columns
-borders3 = borders2[['URI', 'Specialty', 'HospitalCode']]
-
-# Check the first few rows of the dataset. Default is 5 rows. 
-borders3.head()
    - -
    -

    Once the dataframe has been read in, you can delete a specific column -(or columns) that you do not need using del -command:

    -
    -
    # Delete URI column
-del borders2["URI"]
-
-# Check the first few rows of the dataset. Default is 5 rows. 
-borders2.head()
    - -
    -
    -
    -

    Knowledge Check

    -
    Quiz
    -
    -
    -
    -
    -
    - -
    -
    -
    -
    -

    Code Exercise

    -
    -
    # Hello World example
-hello_world <- "Hello World"
-
-print(hello_world)
    - -
    -
    -
    -
    -

    Explore

    -
    -

    Mean/Median & Summary

    -
      -

    • mean() and median() are passed arrays -of values (usually from a data frame) to return the mean and median -value.

      • -

    • describe() returns all summary statistics based on a -given array.

      • -
    -

    For example, -<df_name>["<col_name>"].median() will generate -the median value of the values within the stated -column.

    -

    In the exercise below, you have the borders data-set loaded as -borders_data. See if you can get the mean value for -LengthOfStay, store it in a variable, and print that -variable. Use the hint button if you need some help.

    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data
    - -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data["LengthOfStay"]
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data["LengthOfStay"].mean()
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-mean_value = borders_data["LengthOfStay"].mean()
-
-print(mean_value)
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-mean_value = borders_data["LengthOfStay"].mean()
-
-print(mean_value)
    -
    -
    -
    -

    Frequencies & Crosstabs

    -
      -
    • Frequency: -<df_name>["<col_name>").value_counts()]
      • -
    • Crosstab: -pd.crosstab(<df_name>["<col_1_name>"], <df_name>["<col_2_name>"])
      • -
    • Crosstab & Add Col/Row Totals: -pd.crosstab(<df_name>["<col_1_name>"], <df_name>["<col_2_name>"], margins = True) -tab
      • -
    -

    Create a crosstab for HospitalCode and Sex, -add column and row totals, store the table in a variable, and print it -out. Use the hint button if you need some help.

    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    - -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-...pd.crosstab(...)
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-crosstab_hospitalcode_sex = pd.crosstab(
-  borders_data["HospitalCode"], borders_data["Sex"]...)
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-crosstab_hospitalcode_sex = pd.crosstab(
-  borders_data["HospitalCode"], borders_data["Sex"], margins = True)
-print(crosstab_hospitalcode_sex)
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-crosstab_hospitalcode_sex = pd.crosstab(
-  borders_data["HospitalCode"], borders_data["Sex"], margins = True)
-
-print(crosstab_hospitalcode_sex)
    -
    -
    -
    -
    -

    Wrangle – Part 1

    -

    For the next sections, we will focus on using the pandas module -manipulate data and data frames.

    -
    -

    Location

    -

    There are multiple ways to wrangle data in Python - some are more -straightforward than others.

    -

    To begin efficiently locating and manipulating data, it would be -beneficial to become familiar with the concept of locating data in a -dataframe.

    -

    The loc and iloc attributes are used for -indexing, which allows for the locating of data from a dataframe based -on row and column labels/indexes.

    -

    loc - Label-based indexing (such as Hospital Codes, -Names, etc.).
    iloc - Integer-based indexing (remember -Python indexes count from 0).

    -

    By default, loc and iloc locates row -indexes, instead of column indexes.

    -

    <df_name>.loc['<row_label>'] will select the -first row with row_label as the label index.
    -<df_name>.iloc['<row_index_label>'] will select -the row at index == row_index_label.

    -

    This does not mean the location attributes are isolated to only -locating rows - in fact, the real power of the location attributes is in -its ability to freely locate relevant rows, columns, values, etc.

    -

    It would further be helpful to remember that in some methods, the -axis stated will indicate whether it is targetting a row or -a column.

    -

    axis = 0 = row
    axis = 1 = column

    -

    In the next few sections, we will see how useful the location -attributes can be.

    -
    -
    -

    Select / Slice Out Columns

    -

    By far, the most straightforward way to select a column is by the -following method:

    -
      -
    • Selecting/Slicing Out a column: -<df_name>["<col_name>"]
      • -
    -

    However, by using loc and iloc, we can -start to specify further:

    -
      -

    • Selecting/Slicing Out a column: -<df_name>.loc["<row_index>","<column_index>"]

      • -

    • Example: borders_data.loc[:,"Specialty"] will obtain -all the rows from the Specialty column.

      • -
    -

    You may be wondering why using the loc attribute is -helpful here when the first method is much more simple.

    -

    Using loc, you have the ability to select specific rows -within that column

    -
      -
    • Example: borders_data.loc[0:2,"HospitalCode"] will -obtain the first 3 rows from the HospitalCode column.
      • -
    -

    Read in the data stored in the borders_inc_age csv file, select/slice -out the entire HospitalCode column using pandas, and print -out the selected column.

    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-column_hospital_code =
    - -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-column_hospital_code = borders_data.loc
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-column_hospital_code = borders_data.loc[:,]
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-column_hospital_code = borders_data.loc[:,'HospitalCode']
-
-print(column_hospital_code)
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-column_hospital_code = borders_data.loc[:,'HospitalCode']
-
-print(column_hospital_code)
    -
    -
    -
    -

    Select Rows

    -

    The pandas library has multiple way to select rows, depending on your -criteria.

    -
      -

    • Select Rows by index: -<df_name>.iloc[<index_number>]']

      • -

    • Example: borders_data.iloc[0] This will show the -first row of the dataframe

      • -

    • Select Rows which have a specific value in a specific column: -<df_name>[<df_name>['<column_name>'] == '<row_label>']

      • -

    • Example: -borders_data[borders_data['HospitalCode'] == 'B120H'] -This will show all the rows which have B120H in the -HospitalCode column

      • -

    • Select Rows by index: -<df_name>.loc['<row_label>']']

      • -

    • Example: borders_data.loc['row_label'] This will -locate the first instance of the row with row_label as the label index - -please note, this csv file does not have row labels

      • -
    -

    Read in the borders_inc_age csv file, and select all the rows with -Specialty == A1.

    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    - -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-rows_of_a1 = borders_data[...]
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-rows_of_a1 = borders_data[borders_data['...']...]
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-rows_of_a1 = borders_data[borders_data['Specialty']=='A1']
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-rows_of_a1 = borders_data[borders_data['Specialty']=='A1']
    -
    -
    -
    -

    Add a New Row/Column

    -

    Adding a new row/column is straightforward.

    -

    When adding a new row, you can first create a dictionary, where -keys (column names) and values (corresponding -values in the new row) are mapped together.

    -
      -
    • Create dictionary of mapped keys (columns) and values (row values): -new_row_data = {'<column_name>':'<row_value>','<column_name>':'<row_value>','<column_name>':'<row_value>'}
      • -
    -

    Then, you can append the new row into the dataframe.

    -
      -
    • <df_name> = <df_name>.append(new_row_data)
      • -
    -

    If you want to leave the value blank, you can input None -into the row_value.

    -
      -
    • Add new column: -<df_name>['<new_column_name>'] = <row_value>
      • -
    -

    This will create a new column with all the rows populated by the -row_value.

    -

    Read in the borders_inc_age csv file. Create a new -column called number_of_eyebrows with the row_value as -2 for all the rows.

    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    - -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data[...] ...
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data['number_of_eyebrows'] = ....
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data['number_of_eyebrows'] = 2
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data['number_of_eyebrows'] = 2
    -
    -
    -
    -

    Delete a Row/Columns

    -

    There are multiple ways to delete rows and columns.

    -

    When deleting a row, you could create a dataframe with the rows -containing certain row_values filtered out:

    -
      -
    • Deleting a row: -<df_name> = <df_name>[<df_name>['<column_name>'] != '<row_value>']
      • -
    -

    You can also delete rows based on their index and row_label by -slicing the locations.

    -
      -
    • Removing the first row: -<df_name> = <df_name>.iloc[1:]
      • -
    -

    If we want to remove only the 2nd row using the same method, you -would need to locate the specific rows you want to keep, and append them -together.

    -
      -
    • Removing the second row: -<df_name> = <df_name>.iloc[:1].append(<df_name>.iloc[2:]
      • -
    -

    Alternatively, you could use the drop() method.

    -
      -

    • Removing the second row: -<df_name> = <df_name>.drop(<df_name>.index[1])

      • -

    • Removing the second row row_label: -<df_name> = <df_name>.drop('<row_label>', axis=0) -axis=0 specifies you are removing a -row.

      • -

    • Removing the second row by index position: -<df_name> = <df_name>.drop(index_position, axis=0)

      • -
    -

    The drop() method can also be used to drop columns.

    -
      -

    • Removing a column by label: -<df_name> = <df_name>.drop('<column_name>', axis=1) -axis=1 specifies you are removing a -column.

      • -

    • Removing a column by index position: -<df_name> = <df_name>.drop(<df_name>.columns[index_position], axis=1)

      • -
    -

    Read in the borders_inc_age csv. Remove all the rows which have -Male as the value in the Sex column.

    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    - -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data = ...
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data = borders_data[...]
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data = borders_data[borders_data[...] != ...]
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data = borders_data[borders_data['Sex'] != 'Male']
    -
    -
    -
    -
    -

    Wrangle – Part 2

    -
    -

    Manipulate Strings

    -

    There are a number of ways to manipulate strings in Python. Here are -a few methods to achieve various outputs:

    -
      -

    • Split values at a delimiter: -<df_name>['<column_name>'] = <df_name>['<column_name>'].str.split('<delimiter>')

      • -

    • Example: -borders_data['ManagementofPatient'] = borders_data['ManagementofPatient'].str.split('a')

      • -
    -

    After the above line of code, if you -print(borders_data), you will see that all the values in -the column ManagementofPatient has been split at the value -‘a’.

    -
      -
    • Strip values of leading and trailing white space: -<df_name>['<column_name>'] = <df_name>['<column_name>'].str.strip()
      • -
    • Convert values to upper case: -<df_name>['<column_name>'] = <df_name>['<column_name>'].str.upper()
      • -
    • Convert values to lower case: -<df_name>['<column_name>'] = <df_name>['<column_name>'].str.lower()
      • -
    • Replace values: -<df_name>['<column_name>'] = <df_name>['<column_name>'].str.replace('<old_value>','<new_value>')
      • -
    -

    It is important to note that this is not an exhaustive list of all -the string manipulation methods - there are many more to find out and -use, depending on your specification requirements.

    -

    In the exercise below, read the borders_inc_age csv in, then replace -all instances of B120H in the HospitalCode -column with the phrase BRILLIANT.

    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
    - -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data['HospitalCode'] = ...
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data['HospitalCode'] = borders_data['HospitalCode']...
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT')
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-borders_data['HospitalCode'] = borders_data['HospitalCode'].str.replace('B120H','BRILLIANT')
    -
    -
    -
    -

    Recode

    -

    Another way to recode data which offers more flexibility is by using -the loc attribute.

    -
      -
    • <df_name>.loc[<df_name>['<column_name>'] == <existing_value>, '<column_name>'] = <new_value>
      • -
    -

    This method uses the boolean operator ==, which means -equal to.

    -

    This operator can be substituted with others.

    -

    For example, in the case that you wish to find all the values which -are less than 5 in a given column, and replace them with -Small, you would code the following:

    -
      -
    • data_frame.loc[data_frame['column1'] < 5, 'column1'] = 'Small'
      • -
    -

    As you can imagine, this methods affords a great level of versatility -in data analysis.

    -

    In the exercise below, read in the borders_inc_age csv -file, and recode all instances of B120H in the -HospitalCode column to Recoded.

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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.loc[borders_data[...] = ...
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.loc[borders_data['HospitalCode'] == ..., ...] = 'Replaced'
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.loc[borders_data['HospitalCode'] == 'B120H', 'HospitalCode'] = 'Replaced'
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.loc[borders_data['HospitalCode'] == 'B120H', 'HospitalCode'] = 'Replaced'
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    Rename

    -

    Renames specific columns in a data frame:

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    • <data_frame>.rename(columns={'<old_column_name>':'<new_column_name>'})
      • -
    -

    The column HospitalCode in borders_data -doesn’t follow the style guide, rename it to -Hospital_Code.

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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.rename(...)
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.rename(columns={...})
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.rename(columns={'HospitalCode':'Hospital_Code'})
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.rename(columns={'HospitalCode':'Hospital_Code'})
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    Group By

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    The groupby() function in Pandas allows us to split a -Dataframe into group based on one or more keys, such as column values, -and then perform operations on them, such as aggregations or -transformations.

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    • <dataframe>.groupby('<column_name>')
      • -
    -

    Read in the borders_inc_age csv. Then, apply the -groupby() function to the HospitalCode -column.

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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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-borders_data.groupby('HospitalCode')
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    Aggregate Dataframe

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    With the groupby() function, we can apply the aggregate -agg() function in conduct operations on specific -columns.

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    • <grouped_variable>.agg({<'column'> : <'function'>})
      • -
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    Multiple operations can also be carried out.

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    • <grouped_variable>.agg({<'column'> : [<'function1'>, <'function2'>,<'function3'>]})
      • -
    -

    Multiple columns can aggregated.

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      -
    • <grouped_variable>.agg({<'column1'> : [<'function1'>, <'function2'>,<'function3'>],<'column2'> : [<'function1'>, <'function2'>,<'function3'>]})
      • -
    -

    For example, let’s store the above -borders_data.groupby('HospitalCode') into a variable called -grouped_by_hospital_code.

    -

    We can now find the mean, max, -count and sum of the LengthOfStay -variables, grouped by HospitalCode.

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    • grouped_by_hospital_code.agg({'LengthOfStay': ['mean','max','count','sum']})
      • -
    -

    We could also more easily use the describe() function -within the agg() function detailed above.

    -

    Give that a go: Read in the borders_inc_age csv file, -then group by HospitalCode, storing that into a variable -called grouped_by_hospital_code. Aggregate using the method -above. Use the describe() function to find important values -from the LengthOfStay column.

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    borders_data = pd.read_csv("data/borders_inc_age.csv")
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-group_by_hospital_code = borders_data.groupby('HospitalCode')
-
-...
    -
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-group_by_hospital_code = borders_data.groupby('HospitalCode')
-
-group_by_hospital_code.agg(
-  ...)
    -
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-group_by_hospital_code = borders_data.groupby('HospitalCode')
-
-group_by_hospital_code.agg(
-  {'LengthOfStay':'describe'})
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    borders_data = pd.read_csv("data/borders_inc_age.csv")
-
-group_by_hospital_code = borders_data.groupby('HospitalCode')
-
-group_by_hospital_code.agg(
-  {'LengthOfStay':'describe'})
    -
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    Merge Dataframe

    -

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      -
    • left - taking all records from the “left”, -data_frame_1, and adding matched records from the “right”, -data_frame_2, introducing NaN where the -“right”, data_2 had no matching records
      • -
    • right - taking all records from the “right”, -data_frame_2, and adding matched records from the “left”, -data_frame_1, introducing NaN where the -“left”, data_frame_1 had no matching records
      • -
    • inner - producing only records where there were matches -on both given data sets
      • -
    • full - retaining all records from both data sets and -introducing na on either one where there was no matching -records
      • -
    -

    By default, the merge type will be inner unless stated -otherwise.

    -

    You define the merge/join type by defining the -how = '<type>'.

    -

    You should further define what common columns/variables the data -frames should merge on by defining the -on = ['<insert_column>']

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      -
    • <data_frame_1>.merge(<data_frame_2>, how = '<join_type>', on = ['<insert_column_1>' , '<insert_column_2>'])
      • -
    -

    2 new data-sets have been loaded, baby5 and -baby6, these have common variables FAMILYID -and DOB. Using a left join, merge them -together.

    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-baby5 = pd.read_csv("data/Baby5.csv")
-baby6 = pd.read_csv("data/Baby6.csv")
    - -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-baby5 = pd.read_csv("data/Baby5.csv")
-baby6 = pd.read_csv("data/Baby6.csv")
-
-baby5.merge(...)
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    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-baby5 = pd.read_csv("data/Baby5.csv")
-baby6 = pd.read_csv("data/Baby6.csv")
-
-baby5.merge(baby6, how = '...', on = [...])
    -
    -
    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-baby5 = pd.read_csv("data/Baby5.csv")
-baby6 = pd.read_csv("data/Baby6.csv")
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-baby5.merge(baby6, how = 'left', on = ['FAMILYID','DOB'])
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    -
    borders_data = pd.read_csv("data/borders_inc_age.csv")
-baby5 = pd.read_csv("data/Baby5.csv")
-baby6 = pd.read_csv("data/Baby6.csv")
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-baby5.merge(baby6, how = 'left', on = ['FAMILYID','DOB'])
    -
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    -
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