Overview

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. (source)

Installing Pandas

Using Anaconda

$ conda install pandas

Using PyPI:

$ pip install pandas

Reading Data

Pandas comes with functionality to read data from many file types including:

• CSV

• Excel

• HTML (tables)

• SQL

• BiqQuery

• etc

For most file-based reading, Pandas will accept as the first argument a local file (e.g. pd.read_csv('data.csv') ), or a web URL (e.g. pd.read_csv('https://domain.com/data.csv') ).

CSV

For CSVs, Pandas handles the datatype setting for you. The most common named parameter sent with the read_csv method is skiprows=1 when the inputted CSV has non-heading rows prior to the first row.

import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/airline-safety/airline-safety.csv')
df.head()

Excel

For me, I use the read_excel method much less frequently than read_csv mostly due to habit and the assumption that the data is cleaner with a CSV. Common named parameters would be sheet_name = "Sheet1" or skip_rows=[1].

import pandas as pd
df = pd.read_excel('https://www2.census.gov/programs-surveys/popest/tables/2010-2019/state/totals/nst-est2019-01.xlsx', skiprows=3)
df.head()

HTML Tables

This is a very handy function, where there is a table on a web page that you want to grab the data from.

import pandas as pd
df = pd.read_html('https://countrycode.org/', attrs = {'class': 'main-table'})
df[0].head()

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