Pandas

The Pandas library is built on NumPy and provides easy-to-use data structures and data analysis tools for the Python programming language. Use the following import convention:

import pandas as pd

Pandas Data Structures

Series

A one-dimensional labeled array A capable of holding any data type

s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd'])

DataFrame

A two-dimensional labeled data structure with columns of potentially different types

data = {'Country': ['Belgium', 'India', 'Brazil'],
'Capital': ['Brussels', 'New Delhi', 'Brasília'],
'Population': [11190846, 1303171035, 207847528]}

df = pd.DataFrame(data,
columns=['Country', 'Capital', 'Population'])

I/O

Read and Write to CSV

pd.read_csv('file.csv', header=None, nrows=5)
pd.to_csv('myDataFrame.csv')

Read and Write to Excel

pd.read_excel('file.xlsx')
pd.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1')

Read multiple sheets from the same file

xlsx = pd.ExcelFile('file.xls')
df = pd.read_excel(xlsx, 'Sheet1')

Read and Write to SQL Query or Database Table

from sqlalchemy import create_engine
engine = create_engine('sqlite:///:memory:')
pd.read_sql("SELECT * FROM my_table;", engine)
pd.read_sql_table('my_table', engine)
pd.read_sql_query("SELECT * FROM my_table;", engine)

read_sql()is a convenience wrapper around read_sql_table() and read_sql_query()

pd.to_sql('myDf', engine)

Asking For Help

help(pd.Series.loc)

Selection

Getting

Get one element

>>> s['b'] 

-5

Get subset of a DataFrame

>>> df[1:] Get subset of a DataFrame

Country Capital Population
1 India New Delhi 1303171035
2 Brazil Brasília 207847528

Selecting, Boolean Indexing & Setting

By Position

Select single value by row & column

>>> df.iloc([0],[0])
'Belgium'

>>> df.iat([0],[0])
'Belgium'

By Label

Select single value by row & column labels

>>> df.loc([0], ['Country']) 
'Belgium' 
>>> df.at([0], ['Country']) 
'Belgium'

By Label/Position

Select single row of subset of rows

>>> df.ix[2]
Country Brazil 
Capital Brasília 
Population 207847528

Select a single column of subset of columns

>>> df.ix[:,'Capital']
0 Brussels 
1 New Delhi
2 Brasília

Select rows and columns

>>> df.ix[1,'Capital'] 
'New Delhi'

Boolean Indexing

Series s where value is not >1

>>> s[~(s > 1)]

s where value is <-1 or >2

>>> s[(s < -1) | (s > 2)]

Use filter to adjust DataFrame

>>> df[df['Population']>1200000000]

Setting

Set index a of Series s to 6

>>> s['a'] = 6

Dropping

Drop values from rows (axis=0)

>>> s.drop(['a', 'c'])

Drop values from columns(axis=1)

>>> df.drop('Country', axis=1)

Sort & Rank

Sort by row or column index

>>> df.sort_index(by='Country')

Sort a series by its values

>>> s.order()

Assign ranks to entries

>>> df.rank()

Retrieving Series/DataFrame Information

Basic Information

(rows,columns)

>>> df.shape

Describe index

>>> df.index

Describe DataFrame columns

>>> df.columns

Info on DataFrame

>>> df.info()

Number of non-NA values

>>> df.count()

Summary

Sum of values

>>> df.sum()

Cummulative sum of values

>>> df.cumsum()

Minimum/maximum values

>>> df.min()/df.max()

Minimum/Maximum index value

>>> df.idmin()/df.idmax()

Summary statistics

>>> df.describe()

Mean of values

>>> df.mean()

Median of values

>>> df.median()

Applying Functions

>>> f = lambda x: x*2

Apply function

>>> df.apply(f)

Apply function element-wise

>>> df.applymap(f)

Data Alignment

Internal Data Alignment

NA values are introduced in the indices that don’t overlap:

>>> s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd'])
>>> s + s3
 a 10.0
 b NaN
 c 5.0
 d 7.0

Arithmetic Operations with Fill Methods

You can also do the internal data alignment yourself with the help of the fill methods:

>>> s.add(s3, fill_value=0)
 a 10.0
 b -5.0
 c 5.0
 d 7.0
>>> s.sub(s3, fill_value=2)
>>> s.div(s3, fill_value=4)
>>> s.mul(s3, fill_value=3)

Pandas cheat sheet - Summary

I hope this Pandas cheat sheet has been helpful. The Source for this cheat sheet is available here.