Pandas Cheat Sheet – Python for Data Science

Vik Paruchuri Data Analytics, Libraries, NumPy, Pandas, SQL, Statistics

Pandas is arguably the most important Python package for data science. Not only does it give you lots of methods and functions that make working with data easier, but it has been optimized for speed which gives you a significant advantage compared with working with numeric data using Python’s built-in functions.

The printable version of this cheat sheet

It’s common when first learning pandas to have trouble remembering all the functions and methods that you need, and while at Dataquest we advocate getting used to consulting the pandas documentation, sometimes it’s nice to have a handy reference, so we’ve put together this cheat sheet to help you out!

If you’re interested in learning pandas, you can consult our two-part pandas tutorial blog post, or you can signup for free and start learning pandas through our interactive pandas for data science course.

Key and Imports

In this cheat sheet, we use the following shorthand:

df Any pandas DataFrame object
s Any pandas Series object

You’ll also need to perform the following imports to get started:

import pandas as pd
import numpy as np

Importing Data

pd.read_csv(filename) From a CSV file
pd.read_table(filename) From a delimited text file (like TSV)
pd.read_excel(filename) From an Excel file
pd.read_sql(query, connection_object) Read from a SQL table/database
pd.read_json(json_string) Read from a JSON formatted string, URL or file.
pd.read_html(url) Parses an html URL, string or file and extracts tables to a list of dataframes
pd.read_clipboard() Takes the contents of your clipboard and passes it to read_table()
pd.DataFrame(dict) From a dict, keys for columns names, values for data as lists

Exporting Data

df.to_csv(filename) Write to a CSV file
df.to_excel(filename) Write to an Excel file
df.to_sql(table_name, connection_object) Write to a SQL table
df.to_json(filename) Write to a file in JSON format

Create Test Objects

Useful for testing code segements

pd.DataFrame(np.random.rand(20,5)) 5 columns and 20 rows of random floats
pd.Series(my_list) Create a series from an iterable my_list
df.index = pd.date_range('1900/1/30', periods=df.shape[0]) Add a date index

Viewing/Inspecting Data

df.head(n) First n rows of the DataFrame
df.tail(n) Last n rows of the DataFrame
df.shape() Number of rows and columns
df.info() Index, Datatype and Memory information
df.describe() Summary statistics for numerical columns
s.value_counts(dropna=False) View unique values and counts
df.apply(pd.Series.value_counts) Unique values and counts for all columns

Selection

df[col] Return column with label col as Series
df[[col1, col2]] Return Columns as a new DataFrame
s.iloc[0] Selection by position
s.loc['index_one'] Selection by index
df.iloc[0,:] First row
df.iloc[0,0] First element of first column

Data Cleaning

df.columns = ['a','b','c'] Rename columns
pd.isnull() Checks for null Values, Returns Boolean Arrray
pd.notnull() Opposite of pd.isnull()
df.dropna() Drop all rows that contain null values
df.dropna(axis=1) Drop all columns that contain null values
df.dropna(axis=1,thresh=n) Drop all rows have have less than n non null values
df.fillna(x) Replace all null values with x
s.fillna(s.mean()) Replace all null values with the mean (mean can be replaced with almost any function from the statistics section)
s.astype(float) Convert the datatype of the series to float
s.replace(1,'one') Replace all values equal to 1 with 'one'
s.replace([1,3],['one','three']) Replace all 1 with 'one' and 3 with 'three'
df.rename(columns=lambda x: x + 1) Mass renaming of columns
df.rename(columns={'old_name': 'new_ name'}) Selective renaming
df.set_index('column_one') Change the index
df.rename(index=lambda x: x + 1) Mass renaming of index

Filter, Sort & Groupby

df[df[col] > 0.5] Rows where the col column is greater than 0.5
df[(df[col] > 0.5) & (1.7)] Rows where 0.7 > col > 0.5
df.sort_values(col1) Sort values by col1 in ascending order
df.sort_values(col2,ascending=False) Sort values by col2 in descending order
df.sort_values([col1,ascending=[True,False]) Sort values by col1 in ascending order then col2 in descending order
df.groupby(col) Return a groupby object for values from one column
df.groupby([col1,col2]) Return groupby object for values from multiple columns
df.groupby(col1)[col2] Return the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics section)
df.pivot_table(index=col1,values=[col2,col3],aggfunc=max) Create a pivot table that groups by col1 and calculates the mean of col2 and col3
df.groupby(col1).agg(np.mean) Find the average across all columns for every unique col1 group
data.apply(np.mean) Apply a function across each column
data.apply(np.max,axis=1) Apply a function across each row

Join/Comine

df1.append(df2) Add the rows in df1 to the end of df2 (columns should be identical)
df.concat([df1, df2],axis=1) Add the columns in df1 to the end of df2 (rows should be identical)
df1.join(df2,on=col1,how='inner') SQL-style join the columns in df1 with the columns on df2 where the rows for col have identical values. how can be one of 'left', 'right', 'outer', 'inner'

Statistics

These can all be applied to a series as well.

df.describe() Summary statistics for numerical columns
df.mean() Return the mean of all columns
df.corr() Finds the correlation between columns in a DataFrame.
df.count() Counts the number of non-null values in each DataFrame column.
df.max() Finds the highest value in each column.
df.min() Finds the lowest value in each column.
df.median() Finds the median of each column.
df.std() Finds the standard deviation of each column.

Download a printable version of this cheat sheet

If you’d like to download a printable version of this cheat sheet you can do so below.