Dylan  Iqbal

Dylan Iqbal


Pandas Cheat Sheet for Data Science in Python

A quick guide to the basics of the Python data analysis library Pandas, including code samples.

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.

That’s where this Pandas cheat sheet might come in handy.

It’s a quick guide through the basics of Pandas that you will need to get started on wrangling your data with Python.

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.

Pandas Cheat Sheet for Data Science in Python

The Pandas cheat sheet will guide you through the basics of the Pandas library, going from the data structures to I/O, selection, dropping indices or columns, sorting and ranking, retrieving basic information of the data structures you’re working with to applying functions and data alignment.

In short, everything that you need to kickstart your data science learning with Python!

Python For Data Science Cheat Sheet: Pandas Basics

Use the following import convention:

>>> import pandas as pd

Pandas Data Structures


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

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


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

>>> data = {'Country': ['Belgium',  'India',  'Brazil'],

'Capital': ['Brussels',  'New Delhi',  'Brasilia'],

'Population': [11190846, 1303171035, 207847528]}



2IndiaNew Delhi1303171035

Please note that the first column 1,2,3 is the index and Country,Capital,Population are the Columns.

Asking For Help

>>> help(pd.Series.loc)


Read and Write to CSV

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

Read multiple sheets from the same file

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

Read and Write to Excel

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

Read and Write to SQL Query or Database Table

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

>>> 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)
>>> df.to_sql('myDf', engine)



Get one element

>>> s['b']

Get subset of a DataFrame

>>> df[1:]
Country     Capital   Population
1  India    New Delhi 1303171035
2  Brazil   Brasilia  207847528

Selecting’, Boolean Indexing and Setting

By Position

Select single value by row and and column

>>> df.iloc([0], [0])
>>> df.iat([0], [0])

By Label

Select single value by row and column labels

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

By Label/Position

Select single row of subset of rows

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

Select a single column of subset of columns

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

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]


Set index a of Series s to 6

>>> s['a'] = 6


Drop values from rows (axis=0)

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

Drop values from columns(axis=1)

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

Sort and Rank

Sort by labels along an axis

>>> df.sort_index()

Sort by the values along an axis

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

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()


Sum of values

>>> df.sum()

Cumulative sum of values

>>> df.cumsum()

Minimum/maximum values

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

Minimum/Maximum index value

>>> df.idxmin()/df.idxmax() 

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)

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)

Original article source at https://www.datacamp.com

#python #pandas #datascience #machinelearning

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