PPOL 5203 - Data Science I: Foundations

Week 4: From Nested Lists to Dataframes - Numpy and Intro do Pandas

Professor: Tiago Ventura

Where we are ….

  • We started with the basics of being a data scientist

  • Then we moved over to the primitives of Python as your main DS tool:

    • OOP, Native Data Types in Python

    • Python Libraries, Loops, Functions, Generators, Comprehension….

  • Today we start our journey working with tabular data - a favorite of social scientists!

Plans for Today

  • File Management in Python (Most pythonic way to load data in Python)

  • Data as Nested Lists: Motivating Numpy

  • Numpy

  • Intro do Pandas

    • Series

    • Acesssing Pandas elements

    • Creating DataFrames

  • Discuss your final project.

File Management: How do we read files from our computer into our Python Environment?

  • Connection management functions:

    • open(), iterate over, and close()

  • Reading/writing files

  • Using with() to manage connections.

Summary of file management

  • open(): opens a connection with files on our system.

    • open() returns a special item type *_io.TextIOWrapper*
    • This item is a iterator. We need to go through to convert inputs to a object in python.

  • close(): closes the connection.

  • write(): writes files on your system. Also line by line, as in open()

  • with(): wrapper for open and close that allows alias.

Example:

# Load libraries
import csv # convert a .csv to a nested list
import os  # library for managing our operating system. 

# Read in the gapminder data 
with open("gapminder.csv", mode="rt") as file:
    data = [row for row in csv.reader(file)]

TLDR:

  • Most often we will use high-level functions from Pandas to load data into Python objects.

  • Why are we learning these tools then?

    • Very pythonic ~ see in other people’s code

    • No direct equivalent in R or Stata

    • Important when working non-tabular data - text, json, images, etc..

  • Reading: Check Section 3.3 of Python for Data Analysis to learn more about the topics covered in the notebook.

  • Notebook for File Management

From Nested Data to Dataframes: Motivating Numpy

Motivating Numpy

So far, all the data structures we saw are geared towards unidimensional data.

  • string: sequence of words

  • list: sequence of heterogeneous elements

  • dictionaries: key-value combinations.

Tabular data

Nested Lists

# Read in the gapminder data 
import csv
with open("../lecture_notes/week-04/gapminder.csv",mode="rt") as file:
    data = [row for row in csv.reader(file)]

Quizz

Look at this tabular data organized as Nested List. What is “wrong” here?

# let's see the data
print(data)
[['country', 'lifeExp', 'gdpPercap'], ['Guinea_Bissau', '39.21', '652.157'], ['Bolivia', '52.505', '2961.229'], ['Austria', '73.103', '20411.916'], ['Malawi', '43.352', '575.447'], ['Finland', '72.992', '17473.723'], ['North_Korea', '63.607', '2591.853'], ['Malaysia', '64.28', '5406.038'], ['Hungary', '69.393', '10888.176'], ['Congo', '52.502', '3312.788'], ['Morocco', '57.609', '2447.909'], ['Germany', '73.444', '20556.684'], ['Ecuador', '62.817', '5733.625'], ['Kuwait', '68.922', '65332.91'], ['New_Zealand', '73.989', '17262.623'], ['Mauritania', '52.302', '1356.671'], ['Uganda', '47.619', '810.384'], ['Equatorial Guinea', '42.96', '2469.167'], ['Croatia', '70.056', '9331.712'], ['Indonesia', '54.336', '1741.365'], ['Canada', '74.903', '22410.746'], ['Comoros', '52.382', '1314.38'], ['Montenegro', '70.299', '7208.065'], ['Slovenia', '71.601', '14074.582'], ['Trinidad and Tobago', '66.828', '7866.872'], ['Poland', '70.177', '8416.554'], ['Lesotho', '50.007', '780.553'], ['Italy', '74.014', '16245.209'], ['Tunisia', '60.721', '3477.21'], ['Kenya', '52.681', '1200.416'], ['Gambia', '44.401', '680.133'], ['Bosnia and Herzegovina', '67.708', '3484.779'], ['Libya', '59.304', '12013.579'], ['Greece', '73.733', '13969.037'], ['Ghana', '52.341', '1044.582'], ['Peru', '58.859', '5613.844'], ['Turkey', '59.696', '4469.453'], ['Reunion', '66.644', '4898.398'], ['Sri_Lanka', '66.526', '1854.731'], ['Cambodia', '47.903', '675.368'], ['Bulgaria', '69.744', '6384.055'], ['Lebanon', '65.866', '7269.216'], ['Togo', '51.499', '1153.82'], ['Yemen', '46.78', '1569.275'], ['Jamaica', '68.749', '6197.645'], ['Swaziland', '49.002', '3163.352'], ['Chile', '67.431', '6703.289'], ['Israel', '73.646', '14160.936'], ['Algeria', '59.03', '4426.026'], ['Czech_Republic', '71.511', '13920.011'], ['Djibouti', '46.381', '2697.833'], ['Singapore', '71.22', '17425.382'], ['Nigeria', '43.581', '1488.309'], ['Bangladesh', '49.834', '817.559'], ['DRC', '44.544', '648.343'], ['Cuba', '71.045', '6283.259'], ['Namibia', '53.491', '3675.582'], ['Sudan', '48.401', '1835.01'], ['Syria', '61.346', '3009.288'], ['Rwanda', '41.482', '675.669'], ['Puerto Rico', '72.739', '10863.164'], ['Albania', '68.433', '3255.367'], ['Vietnam', '57.48', '1017.713'], ['Mozambique', '40.38', '542.278'], ['Mali', '43.413', '673.093'], ['Saudi Arabia', '58.679', '20261.744'], ['Liberia', '42.476', '604.814'], ['Madagascar', '47.771', '1335.595'], ['Chad', '46.774', '1165.454'], ['Gabon', '51.221', '11529.865'], ['Mauritius', '64.953', '4768.942'], ['Zambia', '45.996', '1358.199'], ['Romania', '68.291', '7300.17'], ['Dominican Republic', '61.554', '2844.856'], ['Egypt', '56.243', '3074.031'], ['Senegal', '50.626', '1533.122'], ['Oman', '58.443', '12138.562'], ['Zimbabwe', '52.663', '635.858'], ['Botswana', '54.598', '5031.504'], ["Cote d'Ivoire", '48.436', '1912.825'], ['Afghanistan', '37.479', '802.675'], ['Mexico', '65.409', '7724.113'], ['Sao Tome and Principe', '57.896', '1382.782'], ['Myanmar', '53.322', '439.333'], ['Switzerland', '75.565', '27074.334'], ['United Kingdom', '73.923', '19380.473'], ['Japan', '74.827', '17750.87'], ['El Salvador', '59.633', '4431.847'], ['India', '53.166', '1057.296'], ['Thailand', '62.2', '3045.966'], ['Bahrain', '65.606', '18077.664'], ['Australia', '74.663', '19980.596'], ['Mongolia', '55.89', '1692.805'], ['Nepal', '48.986', '782.729'], ['Iran', '58.637', '7376.583'], ['Honduras', '57.921', '2834.413'], ['Guinea', '43.24', '776.067'], ['Venezuela', '66.581', '10088.516'], ['Iceland', '76.511', '20531.422'], ['Somalia', '40.989', '1140.793'], ['Burundi', '44.817', '471.663'], ['Panama', '67.802', '5754.827'], ['Costa Rica', '70.181', '5448.611'], ['Philippines', '60.967', '2174.771'], ['Denmark', '74.37', '21671.825'], ['Benin', '48.78', '1155.395'], ['Eritrea', '45.999', '541.003'], ['Belgium', '73.642', '19900.758'], ['West Bank and Gaza', '60.329', '3759.997'], ['South_Korea', '65.001', '8217.318'], ['Ethiopia', '44.476', '509.115'], ['Guatemala', '56.729', '4015.403'], ['Colombia', '63.898', '4195.343'], ['Cameroon', '48.129', '1774.634'], ['United States', '73.478', '26261.151'], ['Pakistan', '54.882', '1439.271'], ['China', '61.785', '1488.308'], ['Sierra Leone', '36.769', '1072.819'], ['Slovak Republic', '70.696', '10415.531'], ['Tanzania', '47.912', '849.281'], ['Paraguay', '66.809', '3239.607'], ['Argentina', '69.06', '8955.554'], ['Spain', '74.203', '14029.826'], ['Netherlands', '75.648', '21748.852'], ['France', '74.349', '18833.57'], ['Niger', '44.559', '781.077'], ['Central African Republic', '43.867', '958.785'], ['Serbia', '68.551', '9305.049'], ['Iraq', '56.582', '7811.809'], ['Uruguay', '70.782', '7100.133'], ['Angola', '37.883', '3607.101'], ['Sweden', '76.177', '19943.126'], ['Nicaragua', '58.349', '3424.656'], ['South Africa', '53.993', '7247.431'], ['Burkina Faso', '44.694', '843.991'], ['Haiti', '50.165', '1620.739'], ['Norway', '75.843', '26747.307'], ['Taiwan', '70.337', '10224.807'], ['Portugal', '70.42', '11354.092'], ['Jordan', '59.786', '3128.121'], ['Ireland', '73.017', '15758.606'], ['Brazil', '62.239', '5829.317']]

Here comes numpy …

  • Python has no native data structure to work with tabular data (!!!!).

  • Numpy:

    • Introduces arrays (numerical matrices) to the Python world.
    • Optimizes for mathematical operations with matrices.

Why should you learn Numpy? Holds Python together!


Efficiency

  • Numpy leans toward less flexibility and more efficiency.

  • Lists gives you more flexibility and less efficiency.

  • Allows for easy vectorization of functions

  • Broadcasting for working with arrays with different dimensions

Materials

Coding:

Pandas

Motivation

  • Numpy offers a great flexibility and efficiency when dealing with data matrices.

  • Really efficient for mathematical operations.

  • Pretty bad for data wrangling tasks ~> numpy only accepts the same data type

  • The pandas package was designed to solve this limitation by providing data structures to deal with rectangular & heterogeneous data types.

  • Main Data Structures: pd.series() and pd.DataFrames()

Pandas Series

A pandas series is a one-dimensional labeled array.

  • Capable of holding different data types (e.g. integer, boolean, strings, etc.).

  • It holds two key components:

    • index: names in the axis
    • values: values in the series

  • A pandas series is nothing but a column in an excel sheet or an R data.frame (with an index)

Pandas Series Constructor

import pandas as pd

s = pd.Series(["Argentina", "France", "Germany","Spain", "Italy", "Brazil"],
                 index=[2022, 2018, 2014, 2010, 2006, 2002])
print(s)
2022    Argentina
2018       France
2014      Germany
2010        Spain
2006        Italy
2002       Brazil
dtype: object

  • You can feed to the constructor:

    • list

    • dictionaries

    • scalar values

    • ndarray

Pandas DataFrames

A pandas DataFrame is a two dimensional, relational data structure with the capacity to handle heterogeneous data types.

  • relational: each column value contained within a row entry corresponds with the same observation.

  • two dimensional: a matrix data structure

  • heterogeneous: different data types can be contained across each column series.

Constructor

import pandas as pd
# create a simple series
series = pd.Series(["Argentina", "France", "Germany","Spain", "Italy", "Brazil"],
                 index=[2022, 2018, 2014, 2010, 2006, 2002])
# create the dataframe
df = pd.DataFrame(series)
print(df)
              0
2022  Argentina
2018     France
2014    Germany
2010      Spain
2006      Italy
2002     Brazil

  • We will discuss:

    • using lists of dictionaries to build dataframes row-wise

    • using dictionary of lists dataframes column-wise

Pandas DataFrames vs R Dataframes

Important concepts:

  • Creating Dataframes
    • row-wise: using a list of dictionaries
    • column-wise: using a dictionaries of lists
  • Indexing for accessing Data Frames in Python
    • No implicit indexing (d[1,2]) will throw you an error.
    • .iloc[] = use the numerical index position to call to locations in the DataFrame.
    • .loc[] = use the labels to call to the location in the data frame.

Notebook for Pandas

In-class Exercise: your homework.

Data science I: Foundations