Snippington Python Pandas Basic

Visual Studio Code extension for Pandas code snippets.

This works with ipynb (Jupyter Notebook) files in Visual Studio Code too.

Prefix	Description
`pd: imp`	Import Pandas
`pd: read-csv`	Read CSV File
`pd: read-csv-chunked`	Read CSV File in Chunks
`pd: read-csv-skiprows`	Read CSV with skipping rows
`pd: stats`	Show aggregate stats.
`pd: stats-custom`	Get Custom Aggregate Stats
`pd: shape`	Get the shape of dataframe
`pd: row-count`	Get the row count of dataframe
`pd: column-count`	Get the column count of dataframe
`pd: first-x-rows`	Get the first x rows of dataframe
`pd: last-x-rows`	Get the last x rows of dataframe
`pd: n-largest`	Get the n largest values of a column in a dataframe.
`pd: n-smallest`	Get the n smallest values of a column in a dataframe.
`pd: set-categories`	Define categories
`pd: frequency-distribution`	Get frequency distribution of values for a column
`pd: data-sample`	Get sample of data from dataframe
`pd: column-select`	Select single column from dataframe
`pd: column-select-multiple`	Select multiple columns from dataframe
`pd: rows-specific-columns`	Select rows by indices for specific column names in dataframe
`pd: rows-by-index`	Select rows from dataframe by indices
`pd: columns-by-index`	Select columns from dataframe by indices
`pd: group-by-count`	Group By with Count Aggregate
`pd: group-by-mean`	Group By with Mean
`pd: window-expanding-mean`	Expanding Window Mean
`pd: window-rolling-mean`	Expanding Window Mean

Prefix	Description
`pd: dataframe-create`	Create custom dataframe
`pd: columns-rename`	Assign new column names to dataframe
`pd: columns-rename-specific`	Assign new column names to specific columns in dataframe
`pd: replace`	Replace values a dataframe.
`pd: columns-drop`	Drop columns by name
`pd: duplicates-drop`	Drop duplicate columns by name
`pd: sort-by-column`	Sort values in dataframe by column
`pd: filter`	Filter dataframe based on condition/match
`pd: filter-by-query`	Filter dataframe by query based on conditions
`pd: apply-all`	Apply function to all of dataframe
`pd: apply-single-column`	Apply function to a specific column
`pd: apply-multiple-columns`	Apply function to multiple columns
`pd: apply-column-lambda`	Apply lambda function to column
`pd: convert-numeric`	Convert all columns to numeric type
`pd: convert-column-numeric`	Convert a single column to numeric type
`pd: convert-columns-numeric`	Convert multiple columns to numeric type
`pd: set-data-types`	Assign data types to columns
`pd: dataframes-concatenate`	Concatenate dataframes
`pd: pivot-table`	Create Pivot table

Prefix	Description
`pd: fill-mean`	Fill missing values with mean values
`pd: fill-median`	Fill missing values with median values
`pd: fill-mode`	Fill missing values with modal values

Prefix	Description
`pd: write-to-excel`	Write dataframe to Excel file
`pd: write-to-csv`	Write dataframe to CSV file

Prefix	Description
`pd: scatter-plot`	Create a scatter Plot
`pd: bar-plot`	Create a bar plot
`pd: line-plot-multiple`	Create a multiple line plot
`pd: plot-save`	Save plot to output file
`pd: bar-plot-horizontal`	Horizontal bar plot
`pd: histogram`	Make a histogram
`pd: histogram-columns`	Make a histogram for each column

Make sure you have Python, Pandas and Matplotlib installed (optional: for plots only).

pip3 install pandas
pip3 install matplotlib

Pandas Basic Snippets