Python Pandas - Descriptive Statistics


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A large number of methods collectively compute descriptive statistics and other related operations on DataFrame. Most of these are aggregations like sum(), mean(), but some of them, like sumsum(), produce an object of the same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, ...}, but the axis can be specified by name or integer

  • DataFrame − “index” (axis=0, default), “columns” (axis=1)

Let us create a DataFrame and use this object throughout this chapter for all the operations.

Example

import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

#Create a DataFrame
df = pd.DataFrame(d)
print df

Its output is as follows −

    Age  Name   Rating
0   25   Tom     4.23
1   26   James   3.24
2   25   Ricky   3.98
3   23   Vin     2.56
4   30   Steve   3.20
5   29   Smith   4.60
6   23   Jack    3.80
7   34   Lee     3.78
8   40   David   2.98
9   30   Gasper  4.80
10  51   Betina  4.10
11  46   Andres  3.65

sum()

Returns the sum of the values for the requested axis. By default, axis is index (axis=0).

import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

#Create a DataFrame
df = pd.DataFrame(d)
print df.sum()

Its output is as follows −

Age                                                    382
Name     TomJamesRickyVinSteveSmithJackLeeDavidGasperBe...
Rating                                               44.92
dtype: object

Each individual column is added individually (Strings are appended).

axis=1

This syntax will give the output as shown below.

import pandas as pd
import numpy as np
 
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}
 
#Create a DataFrame
df = pd.DataFrame(d)
print df.sum(1)

Its output is as follows −

0    29.23
1    29.24
2    28.98
3    25.56
4    33.20
5    33.60
6    26.80
7    37.78
8    42.98
9    34.80
10   55.10
11   49.65
dtype: float64

mean()

Returns the average value

import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

#Create a DataFrame
df = pd.DataFrame(d)
print df.mean()

Its output is as follows −

Age       31.833333
Rating     3.743333
dtype: float64

std()

Returns the Bressel standard deviation of the numerical columns.

import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

#Create a DataFrame
df = pd.DataFrame(d)
print df.std()

Its output is as follows −

Age       9.232682
Rating    0.661628
dtype: float64

Functions & Description

Let us now understand the functions under Descriptive Statistics in Python Pandas. The following table list down the important functions −

S.No. Function Description
1 count() Number of non-null observations
2 sum() Sum of values
3 mean() Mean of Values
4 median() Median of Values
5 mode() Mode of values
6 std() Standard Deviation of the Values
7 min() Minimum Value
8 max() Maximum Value
9 abs() Absolute Value
10 prod() Product of Values
11 cumsum() Cumulative Sum
12 cumprod() Cumulative Product

Note − Since DataFrame is a Heterogeneous data structure. Generic operations don’t work with all functions.

  • Functions like sum(), cumsum() work with both numeric and character (or) string data elements without any error. Though n practice, character aggregations are never used generally, these functions do not throw any exception.

  • Functions like abs(), cumprod() throw exception when the DataFrame contains character or string data because such operations cannot be performed.

Summarizing Data

The describe() function computes a summary of statistics pertaining to the DataFrame columns.

import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

#Create a DataFrame
df = pd.DataFrame(d)
print df.describe()

Its output is as follows −

               Age         Rating
count    12.000000      12.000000
mean     31.833333       3.743333
std       9.232682       0.661628
min      23.000000       2.560000
25%      25.000000       3.230000
50%      29.500000       3.790000
75%      35.500000       4.132500
max      51.000000       4.800000

This function gives the mean, std and IQR values. And, function excludes the character columns and given summary about numeric columns. 'include' is the argument which is used to pass necessary information regarding what columns need to be considered for summarizing. Takes the list of values; by default, 'number'.

  • object − Summarizes String columns
  • number − Summarizes Numeric columns
  • all − Summarizes all columns together (Should not pass it as a list value)

Now, use the following statement in the program and check the output −

import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

#Create a DataFrame
df = pd.DataFrame(d)
print df.describe(include=['object'])

Its output is as follows −

          Name
count       12
unique      12
top      Ricky
freq         1

Now, use the following statement and check the output −

import pandas as pd
import numpy as np

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

#Create a DataFrame
df = pd.DataFrame(d)
print df. describe(include='all')

Its output is as follows −

          Age          Name       Rating
count   12.000000        12    12.000000
unique        NaN        12          NaN
top           NaN     Ricky          NaN
freq          NaN         1          NaN
mean    31.833333       NaN     3.743333
std      9.232682       NaN     0.661628
min     23.000000       NaN     2.560000
25%     25.000000       NaN     3.230000
50%     29.500000       NaN     3.790000
75%     35.500000       NaN     4.132500
max     51.000000       NaN     4.800000


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