Pandas_CheatSheet.md

Title: Introduction of Basic Pandas Operations

Import the pandas library

import pandas as pd

Create an empty DataFrame

pd.DataFrame()

Create a DataFrame from a dictionary of lists

Each key becomes a column name, and the list elements are the column values

pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

Create a Series from a list

pd.Series([1, 2, 3, 4, 5])

View the first few rows of a DataFrame

Helps to quickly inspect the beginning of large DataFrames

df.head()

View the last few rows of a DataFrame

Useful for checking the end portions of large DataFrames

df.tail()

Select a single column, returns a Series

df['column_name']

Select multiple columns, returns a DataFrame

df[['column1', 'column2']]

Select rows by position (integer-location based)

df.iloc[0] # First row df.iloc[0:5] # First five rows

Select a specific value by row and column position

df.iloc[0, 0] # Value in first row and first column

Filter rows based on column values

df[df['column'] > 10]

Get basic statistics for numerical columns

df.describe()

Get the data types of columns

df.dtypes

Check for missing values

df.isnull()

Drop rows with any missing values

df.dropna()

Fill missing values with a specified value

df.fillna(value)

Create a new column

df['new_column'] = [value1, value2, value3]

Drop a column

df.drop('column_name', axis=1, inplace=True)

Rename columns

df.rename(columns={'old_name': 'new_name'}, inplace=True)

Sum of a column's values

df['column'].sum()

Average (mean) of a column's values

df['column'].mean()

Converts all characters in the column to uppercase

df[‘text_coloumn’].str.upper()

Converts all characters in the column to lowercase

df[‘text_column’].str.lower()

Removes leading and trailing whitespace from each entry in the column

df[‘text_column’].str.strip()

Aligns text to the left

df[‘text_column’].str.ljust(width)

Aligns text to the right

df[‘text_column’].str.rjust(width)

Aligns text to the center

df[‘text_column’].str.center(width)

Read data from a CSV file into a DataFrame

pd.read_csv('filename.csv') #Other types: read_json, read_html, read_xml, read_excel

Write data from a DataFrame to a CSV file

df.to_csv('filename.csv', index=False) #Other types: to_json, to_html, to_xml, to_excel

Sort DataFrame by a column

df.sort_values(by='column_name')

Get unique values from a column

df['column_name'].unique()

Get the number of unique values in a column

df['column_name'].nunique()

Get the count of occurrences of each unique value in a column

df['column_name'].value_counts()

Set a column as the index of the DataFrame

df.set_index('column_name', inplace=True)

Reset the index, turning it back into a column

df.reset_index(inplace=True)

Convert a column's data type

df['column_name'] = df['column_name'].astype('new_type') #e.g. “float32”

Basic plotting (requires matplotlib)

df['column_name'].plot()

Save a plot to a file (requires matplotlib)

df['column_name'].plot().figure.savefig('plot.png')