March 4, 2020

Pandas Cheat Sheet — Python for Data Science

If you\'re interested in working with data in Python, you\'re almost certainly going to be using the pandas library. But even when you\'ve learned pandas — perhaps in our interactive pandas course — it\'s easy to forget the specific syntax for doing something. That\'s why we\'ve created a pandas cheat sheet to help you easily reference the most common pandas tasks.

Before we dive into the cheat sheet, it\'s worth mentioning that you shouldn\'t rely on just this. If you haven\'t learned any pandas yet, we\'d strongly recommend working through our pandas course. This cheat sheet will help you quickly find and recall things you\'ve already learned about pandas; it isn\'t designed to teach you pandas from scratch!

It\'s also a good idea to check to the official pandas documentation from time to time, even if you can find what you need in the cheat sheet. Reading documentation is a skill every data professional needs, and the documentation goes into a lot more detail than we can fit in a single sheet anyway!

If you\'re looking to use pandas for a specific task, we also recommend checking out the full list of our free Python tutorials; many of them make use of pandas in addition to other Python libraries. In our Python datetime tutorial, for example, you\'ll also learn how to work with dates and times in pandas.

Pandas Cheat Sheet: Guide

First, it may be a good idea to bookmark this page, which will be easy to search with Ctrl+F when you\'re looking for something specific. However, we\'ve also created a PDF version of this cheat sheet that you can download from here in case you\'d like to print it out.

In this cheat sheet, we\'ll use the following shorthand:

df | Any pandas DataFrame object s | Any pandas Series object

As you scroll down, you\'ll see we\'ve organized related commands using subheadings so that you can quickly search for and find the correct syntax based on the task you\'re trying to complete.

Also, a quick reminder — to make use of the commands listed below, you\'ll need to first import the relevant libraries like so:

import pandas as pd
import numpy as np

Importing Data

Use these commands to import data from a variety of different sources and formats.

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

Use these commands to export a DataFrame to CSV, .xlsx, SQL, or JSON.

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

These commands can be useful for creating test segments.

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

Use these commands to take a look at specific sections of your pandas DataFrame or Series.

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 | 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

Use these commands to select a specific subset of your data.

df[col] | Returns column with label col as Series df[[col1, col2]] | Returns 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

Use these commands to perform a variety of data cleaning tasks.

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 module) 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, and Groupby

Use these commands to filter, sort, and group your data.

df[df[col] > 0.5] | Rows where the column col is greater than 0.5df[(df[col] > 0.5) & (df[col] < 0.7)] | Rows where 0.7 > col > 0.5df.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,col2],ascending=[True,False]) | Sort values by col1 in ascending order then col2 in descending order df.groupby(col) | Returns a groupby object for values from one column df.groupby([col1,col2]) | Returns groupby object for values from multiple columns df.groupby(col1)[col2] | Returns the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics module) df.pivot_table(index=col1,values=[col2,col3],aggfunc=mean) | Create a pivot table that groups by col1 and calculates the mean of col2 and col3df.groupby(col1).agg(np.mean) | Find the average across all columns for every unique col1 group df.apply(np.mean) | Apply the function np.mean() across each column nf.apply(np.max,axis=1) | Apply the function np.max() across each row ## Join/Combine

Use these commands to combine multiple dataframes into a single one.

df1.append(df2) | Add the rows in df1 to the end of df2 (columns should be identical) pd.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

Use these commands to perform various statistical tests. (These can all be applied to a series as well.)

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

Further Resources

If you’d like to learn more about this topic, check out Dataquest\'s interactive Pandas and NumPy Fundamentals course, and our Data Analyst in Python, and Data Scientist in Python paths that will help you become job-ready in around 6 months.


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