Pandas Basics Cheat Sheet.

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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 to import pandas library:
>>> import pandas as pd

Pandas Data Structures.

Pandas Basics Cheat Sheet. 1

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'])
Pandas Basics Cheat Sheet. 2

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'])

Read and Write to CSV.

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

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