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30 Days Of Python: Day 25 - Pandas

Twitter Follow Author: Asabeneh Yetayeh
Second Edition: July, 2021

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30DaysOfPython

📘 Day 25

Pandas

Pandas is an open source, high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas adds data structures and tools designed to work with table-like data which is Series and Data Frames. Pandas provides tools for data manipulation:

  • reshaping
  • merging
  • sorting
  • slicing
  • aggregation
  • imputation. If you are using anaconda, you do not have install pandas.

Installing Pandas

For Mac: 

pip install conda
conda install pandas

For Windows: 

pip install conda
pip install pandas

Pandas data structure is based on Series and DataFrames.

A series is a column and a DataFrame is a multidimensional table made up of collection of series. In order to create a pandas series we should use numpy to create a one dimensional arrays or a python list. Let us see an example of a series:

Names Pandas Series

pandas series

Countries Series

pandas series

Cities Series

pandas series

As you can see, pandas series is just one column of data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.

Let us see, an example of a pandas data frame:

Pandas data frame

Data frame is a collection of rows and columns. Look at the table below; it has many more columns than the example above:

Pandas data frame

Next, we will see how to import pandas and how to create Series and DataFrames using pandas

Importing Pandas

import pandas as pd # importing pandas as pd
import numpy  as np # importing numpy as np

Creating Pandas Series with Default Index

nums = [1, 2, 3, 4,5]
s = pd.Series(nums)
print(s)

    0    1
    1    2
    2    3
    3    4
    4    5
    dtype: int64

Creating Pandas Series with custom index

nums = [1, 2, 3, 4, 5]
s = pd.Series(nums, index=[1, 2, 3, 4, 5])
print(s)

    1    1
    2    2
    3    3
    4    4
    5    5
    dtype: int64

fruits = ['Orange','Banana','Mango']
fruits = pd.Series(fruits, index=[1, 2, 3])
print(fruits)

    1    Orange
    2    Banana
    3    Mango
    dtype: object

Creating Pandas Series from a Dictionary

dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}

s = pd.Series(dct)
print(s)

    name       Asabeneh
    country     Finland
    city       Helsinki
    dtype: object

Creating a Constant Pandas Series

s = pd.Series(10, index = [1, 2, 3])
print(s)

    1    10
    2    10
    3    10
    dtype: int64

Creating a Pandas Series Using Linspace

s = pd.Series(np.linspace(5, 20, 10)) # linspace(starting, end, items)
print(s)

    0     5.000000
    1     6.666667
    2     8.333333
    3    10.000000
    4    11.666667
    5    13.333333
    6    15.000000
    7    16.666667
    8    18.333333
    9    20.000000
    dtype: float64

DataFrames

Pandas data frames can be created in different ways.

Creating DataFrames from List of Lists

data = [
    ['Asabeneh', 'Finland', 'Helsink'], 
    ['David', 'UK', 'London'],
    ['John', 'Sweden', 'Stockholm']
]
df = pd.DataFrame(data, columns=['Names','Country','City'])
print(df)
Names Country City
0 Asabeneh Finland Helsink
1 David UK London
2 John Sweden Stockholm

Creating DataFrame Using Dictionary

data = {'Name': ['Asabeneh', 'David', 'John'], 'Country':[
    'Finland', 'UK', 'Sweden'], 'City': ['Helsiki', 'London', 'Stockholm']}
df = pd.DataFrame(data)
print(df)
Name Country City
0 Asabeneh Finland Helsiki
1 David UK London
2 John Sweden Stockholm

Creating DataFrames from a List of Dictionaries

data = [
    {'Name': 'Asabeneh', 'Country': 'Finland', 'City': 'Helsinki'},
    {'Name': 'David', 'Country': 'UK', 'City': 'London'},
    {'Name': 'John', 'Country': 'Sweden', 'City': 'Stockholm'}]
df = pd.DataFrame(data)
print(df)
Name Country City
0 Asabeneh Finland Helsinki
1 David UK London
2 John Sweden Stockholm

Reading CSV File Using Pandas

To download the CSV file, what is needed in this example, console/command line is enough:

curl -O https://raw.githubusercontent.com/Asabeneh/30-Days-Of-Python/master/data/weight-height.csv

Put the downloaded file in your working directory.

import pandas as pd

df = pd.read_csv('weight-height.csv')
print(df)

Data Exploration

Let us read only the first 5 rows using head()

print(df.head()) # give five rows we can increase the number of rows by passing argument to the head() method
Gender Height Weight
0 Male 73.847017 241.893563
1 Male 68.781904 162.310473
2 Male 74.110105 212.740856
3 Male 71.730978 220.042470
4 Male 69.881796 206.349801

Let us also explore the last recordings of the dataframe using the tail() methods.

print(df.tail()) # tails give the last five rows, we can increase the rows by passing argument to tail method
Gender Height Weight
9995 Female 66.172652 136.777454
9996 Female 67.067155 170.867906
9997 Female 63.867992 128.475319
9998 Female 69.034243 163.852461
9999 Female 61.944246 113.649103

As you can see the csv file has three rows: Gender, Height and Weight. If the DataFrame would have a long rows, it would be hard to know all the columns. Therefore, we should use a method to know the colums. we do not know the number of rows. Let's use shape meathod.

print(df.shape) # as you can see 10000 rows and three columns

(10000, 3)

Let us get all the columns using columns.

print(df.columns)

Index(['Gender', 'Height', 'Weight'], dtype='object')

Now, let us get a specific column using the column key

heights = df['Height'] # this is now a series

print(heights)

    0       73.847017
    1       68.781904
    2       74.110105
    3       71.730978
    4       69.881796
              ...    
    9995    66.172652
    9996    67.067155
    9997    63.867992
    9998    69.034243
    9999    61.944246
    Name: Height, Length: 10000, dtype: float64

weights = df['Weight'] # this is now a series

print(weights)

    0       241.893563
    1       162.310473
    2       212.740856
    3       220.042470
    4       206.349801
               ...    
    9995    136.777454
    9996    170.867906
    9997    128.475319
    9998    163.852461
    9999    113.649103
    Name: Weight, Length: 10000, dtype: float64

print(len(heights) == len(weights))

True

The describe() method provides a descriptive statistical values of a dataset.

print(heights.describe()) # give statisical information about height data

    count    10000.000000
    mean        66.367560
    std          3.847528
    min         54.263133
    25%         63.505620
    50%         66.318070
    75%         69.174262
    max         78.998742
    Name: Height, dtype: float64

print(weights.describe())

    count    10000.000000
    mean       161.440357
    std         32.108439
    min         64.700127
    25%        135.818051
    50%        161.212928
    75%        187.169525
    max        269.989699
    Name: Weight, dtype: float64

print(df.describe())  # describe can also give statistical information from a dataFrame
Height Weight
count 10000.000000 10000.000000
mean 66.367560 161.440357
std 3.847528 32.108439
min 54.263133 64.700127
25% 63.505620 135.818051
50% 66.318070 161.212928
75% 69.174262 187.169525
max 78.998742 269.989699

Similar to describe(), the info() method also give information about the dataset.

Modifying a DataFrame

Modifying a DataFrame: * We can create a new DataFrame * We can create a new column and add it to the DataFrame, * we can remove an existing column from a DataFrame, * we can modify an existing column in a DataFrame, * we can change the data type of column values in the DataFrame

Creating a DataFrame

As always, first we import the necessary packages. Now, lets import pandas and numpy, two best friends ever.

import pandas as pd
import numpy as np
data = [
    {"Name": "Asabeneh", "Country":"Finland","City":"Helsinki"},
    {"Name": "David", "Country":"UK","City":"London"},
    {"Name": "John", "Country":"Sweden","City":"Stockholm"}]
df = pd.DataFrame(data)
print(df)
Name Country City
0 Asabeneh Finland Helsinki
1 David UK London
2 John Sweden Stockholm

Adding a column to a DataFrame is like adding a key to a dictionary.

First let's use the previous example to create a DataFrame. After we create the DataFrame, we will start modifying the columns and column values.

Adding a New Column

Let's add a weight column in the DataFrame

weights = [74, 78, 69]
df['Weight'] = weights
df
Name Country City Weight
0 Asabeneh Finland Helsinki 74
1 David UK London 78
2 John Sweden Stockholm 69

Let's add a height column into the DataFrame aswell

heights = [173, 175, 169]
df['Height'] = heights
print(df)
Name Country City Weight Height
0 Asabeneh Finland Helsinki 74 173
1 David UK London 78 175
2 John Sweden Stockholm 69 169

As you can see in the DataFrame above, we did add new columns, Weight and Height. Let's add one additional column called BMI(Body Mass Index) by calculating their BMI using thier mass and height. BMI is mass divided by height squared (in meters) - Weight/Height * Height.

As you can see, the height is in centimeters, so we shoud change it to meters. Let's modify the height row.

Modifying column values

df['Height'] = df['Height'] * 0.01
df
Name Country City Weight Height
0 Asabeneh Finland Helsinki 74 1.73
1 David UK London 78 1.75
2 John Sweden Stockholm 69 1.69 
# Using functions makes our code clean, but you can calculate the bmi without one
def calculate_bmi ():
    weights = df['Weight']
    heights = df['Height']
    bmi = []
    for w,h in zip(weights, heights):
        b = w/(h*h)
        bmi.append(b)
    return bmi

bmi = calculate_bmi()

df['BMI'] = bmi
df
Name Country City Weight Height BMI
0 Asabeneh Finland Helsinki 74 1.73 24.725183
1 David UK London 78 1.75 25.469388
2 John Sweden Stockholm 69 1.69 24.158818

Formating DataFrame columns

The BMI column values of the DataFrame are float with many significant digits after decimal. Let's change it to one significant digit after point.

df['BMI'] = round(df['BMI'], 1)
print(df)
Name Country City Weight Height BMI
0 Asabeneh Finland Helsinki 74 1.73 24.7
1 David UK London 78 1.75 25.5
2 John Sweden Stockholm 69 1.69 24.2

The information in the DataFrame seems not yet complete, let's add birth year and current year columns.

birth_year = ['1769', '1985', '1990']
current_year = pd.Series(2020, index=[0, 1,2])
df['Birth Year'] = birth_year
df['Current Year'] = current_year
df
Name Country City Weight Height BMI Birth Year Current Year
0 Asabeneh Finland Helsinki 74 1.73 24.7 1769 2020
1 David UK London 78 1.75 25.5 1985 2020
2 John Sweden Stockholm 69 1.69 24.2 1990 2020

Checking data types of Column values

print(df.Weight.dtype)

    dtype('int64')

df['Birth Year'].dtype # it gives string object , we should change this to number

df['Birth Year'] = df['Birth Year'].astype('int')
print(df['Birth Year'].dtype) # let's check the data type now

    dtype('int32')

Now same for the current year:

df['Current Year'] = df['Current Year'].astype('int')
df['Current Year'].dtype

    dtype('int32')

Now, the column values of birth year and current year are integers. We can calculate the age.

ages = df['Current Year'] - df['Birth Year']
ages

0    251
1     35
2     30
dtype: int32

df['Ages'] = ages
print(df)
Name Country City Weight Height BMI Birth Year Current Year Ages
0 Asabeneh Finland Helsinki 74 1.73 24.7 1769 2019 250
1 David UK London 78 1.75 25.5 1985 2019 34
2 John Sweden Stockholm 69 1.69 24.2 1990 2019 29

The person in the first row lived so far for 251 years. It is unlikely for someone to live so long. Either it is a typo or the data is cooked. So lets fill that data with average of the columns without including outlier.

mean = (35 + 30)/ 2

mean = (35 + 30)/ 2
print('Mean: ',mean)    #it is good to add some description to the output, so we know what is what

   Mean:  32.5

Boolean Indexing

print(df[df['Ages'] > 120])
Name Country City Weight Height BMI Birth Year Current Year Ages
0 Asabeneh Finland Helsinki 74 1.73 24.7 1769 2020 251 
print(df[df['Ages'] < 120])
Name Country City Weight Height BMI Birth Year Current Year Ages
1 David UK London 78 1.75 25.5 1985 2020 35
2 John Sweden Stockholm 69 1.69 24.2 1990 2020 30

Exercises: Day 25

  1. Read the hacker_news.csv file from data directory
  2. Get the first five rows
  3. Get the last five rows
  4. Get the title column as pandas series
  5. Count the number of rows and columns
    • Filter the titles which contain python
    • Filter the titles which contain JavaScript
    • Explore the data and make sense of it

🎉 CONGRATULATIONS ! 🎉

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