Pandas Cheet Seet

The Pandas library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to matplotlib for data visualization and NumPy, the fundamental library for scientific computing in Python on which Pandas was built.

The fast, flexible, and expressive Pandas data structures are designed to make real-world data analysis significantly easier, but this might not be immediately the case for those who are just getting started with it. Exactly because there is so much functionality built into this package that the options are overwhelming.

As such, you can use it as a handy reference if you are just beginning their data science journey with Pandas or, for those of you who already haven’t started yet, you can just use it as a guide to make it easier to learn about and use it.

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
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

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 0.5
df[(df[col] > 0.5) & (df[col] < 0.7)] | 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,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 col3
df.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

source: Dataquest,Data Camp