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Pandas

Part 10: PIVOT TABLES

We will go through the basics of working of Pivot Tables – essentially a multi-dimensional version of GroupBy aggregation

Pivot tables are very much similar to what we experienced in spreadsheets. The difference between pivot tables and GroupBy function: “Pivot table is essentially a multi-dimensional version of GroupBy aggregation.” — that is, you split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid.

import numpy as np
import pandas as pd 
import seaborn as sns

🛳 Titanic dataset for demonstration

# importing dataset for demonstration
titanic = pd.read_csv('data/titanic.csv')
titanic.head()
survivedpclasssexagefareembarkedwhoembark_townalivealone
003male22.07.2500SmanSouthamptonnoFalse
111female38.071.2833CwomanCherbourgyesFalse
213female26.07.9250SwomanSouthamptonyesTrue
311female35.053.1000SwomanSouthamptonyesFalse
403male35.08.0500SmanSouthamptonnoTrue

1. WHAT IF WE USE GROUPBY

1.1. Finding survival rate by Gender

Essentially:

  • group(split) by sex,
  • select survived, and,
  • apply mean
titanic.groupby('sex')['survived'].mean()
sex
female    0.742038
male      0.188908
Name: survived, dtype: float64

1.2. Finding survival rate by Gender and Class

Essentially;

  • group(split) by sex & pclass,
  • select survived column, and,
  • apply mean aggregate
titanic.groupby(['sex','pclass'])['survived'].mean()
sex     pclass
female  1         0.968085
        2         0.921053
        3         0.500000
male    1         0.368852
        2         0.157407
        3         0.135447
Name: survived, dtype: float64
# unstack the result for better presentation
titanic.groupby(['sex','pclass'])['survived'].mean().unstack()
pclass         1         2         3
sex                                 
female  0.968085  0.921053  0.500000
male    0.368852  0.157407  0.135447

**Conclusion: ** Though we can apply two-dimensional Groupby but the code will start to look long-to-read and understand. Pandas have better tool, pivot_table, to deal with this.

2. USING PIVOT TABLE

The above two-dimensional GroupBy result can be easily derived from following pivot_table code. We will use .pivot_table() constructor, whose default aggfunc is np.mean

titanic.pivot_table('survived', index='sex', columns='pclass')
pclass         1         2         3
sex                                 
female  0.968085  0.921053  0.500000
male    0.368852  0.157407  0.135447

We can also get same result without mentioning the index and column kwargs

titanic.pivot_table('survived', 'sex', 'pclass')
pclass         1         2         3
sex                                 
female  0.968085  0.921053  0.500000
male    0.368852  0.157407  0.135447

2.1. Multilevel Pivot Table

Let suppose, we want to group by age, sex and get the survived mean value by each pclass. But instead of a using each age value as separate group, we will make age_groups. To do this, we will first use pd.cut function to make the segment for age column. To make age segments, first let see min and max age in our dataset:

print(f"Min Age: {titanic['age'].min()}")
print(f"Max Age: {titanic['age'].max()}")
Min Age: 0.42
Max Age: 80.0

Lets make two age group: 0-18 and 18-80

age_group = pd.cut(titanic['age'], [0,18,80])
age_group.head()
0    (18, 80]
1    (18, 80]
2    (18, 80]
3    (18, 80]
4    (18, 80]
Name: age, dtype: category
Categories (2, interval[int64]): [(0, 18] < (18, 80]]

Now, we will apply pivot_table on sex and age (through newly created age_group) Other variables will stay the same — finding survived mean value for each pclass

titanic.pivot_table('survived', index=['sex',age_group], columns='pclass')
pclass                  1         2         3
sex    age                                   
female (0, 18]   0.909091  1.000000  0.511628
       (18, 80]  0.972973  0.900000  0.423729
male   (0, 18]   0.800000  0.600000  0.215686
       (18, 80]  0.375000  0.071429  0.133663

2.2. Additional Pivot Table Options

a. Parameters of pivot_table

ParamterDefault
values=None
index=None
aggfunc=‘mean’
margins=False
dropna=True
margins_name=‘all’

b. aggfunc

Let suppose, we want to know the sum of survived and mean of fare columns, in each pclass

titanic.pivot_table(index='sex',columns='pclass', aggfunc={'survived': sum, 'fare': 'mean'})
# omitted the values keyword; 
# when you’re specifying a mapping for aggfunc, this is determined automatically.
                          fare                   survived        
pclass           1          2          3        1   2   3
sex                                                      
female  106.125798  21.970121  16.118810       91  70  72
male     67.226127  19.741782  12.661633       45  17  47

c. margins =True

This simple property margins=True computes sum along each column and row

titanic.pivot_table('survived', index='sex', columns='pclass', margins=True)
pclass         1         2         3       All
sex                                           
female  0.968085  0.921053  0.500000  0.742038
male    0.368852  0.157407  0.135447  0.188908
All     0.629630  0.472826  0.242363  0.383838

Overall, approx. 38% people on board survived

3. CONCEPTS IN PRACTICE: BIRTHRATE DATA

  • First, load the dataset using Pandas read_csv function
  • Then we view the head of the dataset, .head() to get initial sense of dataset
  • To find total rows and columns in the dataset, we will use .shape method
births = pd.read_csv('data/births.csv')
print(births.head())
print(births.shape)
   year  month  day gender  births
0  1969      1  1.0      F    4046
1  1969      1  1.0      M    4440
2  1969      1  2.0      F    4454
3  1969      1  2.0      M    4548
4  1969      1  3.0      F    4548
(15547, 5)

1️⃣ Finding sum of births in each month, across each gender

births.pivot_table('births', index='month', columns='gender', aggfunc='sum', margins=True)
gender         F         M        All
month                                
1        6035447   6328750   12364197
2        5634064   5907114   11541178
3        6181613   6497231   12678844
4        5889345   6196546   12085891
5        6145186   6479786   12624972
6        6093026   6428044   12521070
7        6512299   6855257   13367556
8        6600723   6927284   13528007
9        6473029   6779802   13252831
10       6330549   6624401   12954950
11       5956388   6241579   12197967
12       6184154   6472761   12656915
All     74035823  77738555  151774378

Plotting the results

# using matplotlib to draw figure of 
# sum of births in each month, across each gender
# magic function (%matplotlib) to make the plot appear and store in notebook
%matplotlib inline

import matplotlib.pyplot as plt
sns.set() # set seaborn styles
births.pivot_table('births', index='month', columns='gender', aggfunc='sum').plot()
plt.ylabel('total births in each month');

2️⃣ Finding sum of births in each decade, across each gender

# adding a decade column
births['decade'] = 10 * (births['year'] // 10 ) # //10 will remove the last digit in year 
# creating pivot table for total births, in each decade, along each gender type
print(births.pivot_table('births', index='decade', columns='gender', aggfunc='sum', margins=True))
gender         F         M        All
decade                               
1960     1753634   1846572    3600206
1970    16263075  17121550   33384625
1980    18310351  19243452   37553803
1990    19479454  20420553   39900007
2000    18229309  19106428   37335737
All     74035823  77738555  151774378

Let’s put this table into figure

# using matplotlib to draw figure of 
# sum of births in each decade, across each gender
# magic function (%matplotlib) to make the plot appear and store in notebook
%matplotlib inline

sns.set() # set seaborn styles
births.pivot_table('births', index='year', columns='gender', aggfunc='sum').plot()
plt.ylabel('total births per year');

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