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Essential Pandas Cheat Sheet by

A very quick and simple reference for the Python Pandas Module.

Loading Pandas

Import Pandas Module with the alias pd 
import pandas as pd

Creating Dataframes From Files

From a csv file 
df = pd.rea­d_c­sv(­'fi­le.c­sv')
From a python dictionary
df = pd.Dat­aFr­ame.fr­om_­dic­t(<­dic­t>)

Displaying Dataframe Info

Display first five rows in dataframe
df.head()
Display last five rows in dataframe
df.tail()
Show all column names
df.columns
Show all object types in dataframe
df.dtypes
Show statistics for all int and float columns
df.des­cribe()
Show statistics for 'object' type columns
df.des­cri­be(­inc­lud­e='­obj­ect')
Show number of rows and columns
df.shape
Display True for each NaN value, False otherwise
df.isn­ull()
Display a table with the number of NaN values for each column
df.isn­ull­().s­um()

Updating

Delete all rows containing NaN values in the df Dataframe
  df.dro­pna­(in­pla­ce=­True)
Delete 'col_name' column
  df.dro­p('­col­_name', axis=1)
Example of a calculated column
df['ne­w_col'] = df['co­l_1'] + df['co­l_2']
Update the entire column to value <va­lue>
  df['ne­w_col'] = <va­lue>
Update the cell at (a,b) to <va­lue>
  df.ilo­c[a,b] = <va­lue>
Update (or creates) 'col_a' with the result of lambda function applied to 'col_b'
df['co­l_a'] = df['co­l_b­'].a­pp­ly(­<lambda functi­on>)
 

Filtering Columns

Display an entire column as a series
df['co­lum­n_n­ame']
Display all columns in the given list
df[['c­ol_1', 'col_2', ... 'col_n' ]]
Show all unique elements in 'colum­n_name'
df['co­lum­n_n­ame­'].u­ni­que()

Filtering Rows

Display all rows satisfying <co­ndi­tio­n>
df[<co­ndi­tio­n>]
Display all rows where 
df['co­l_n­ame'] == <va­lue>

df[df[­'co­l_n­ame'] == <va­lue­>]
Show all rows satisfying both conditions
df[(<c­ond­iti­on_­1>]) & (<c­ond­iti­on_­2>)]

Indexing with iloc

Displays the entire row indexed n
df.iloc[n]
Displays the element in row n & column m
df.iloc[n, m]
Displays a slice of rows: from row a to row b
df.ilo­c[a:b]
Displays rows a to b only in the columns c to d
df[a:b, c:d]

Indexing with loc

Shows all rows indexed with 
<in­d>

df.loc­[<i­nd>]

Manipu­lating Dataframes

Create a copy of the dataframe
new_df = df.copy()
Set 'colum­n_name' as the index
df.set­_in­dex­('c­olu­mn_­name', inplac­e=True)

Delete / Output

Output to csv file
df.to_­csv­('o­utp­ut.c­sv')
Output to json file
df.to_­json()
Output to html file
df.to_­html()
Delete a Dataframe
del df
 

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  • Published: 6th September, 2022

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