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Python Pandas - Missing Data

Mohit Sharma

a year ago

Python Pandas Working With Missing Data | InsideAIML
Table of Contents
  • When and Why Is Data Missed?
  • Check for Missing Values
  • Calculations with Missing Data
  • Cleaning / Filling Missing Data
              1. Replace NaN with a Scalar Value
              2. Fill NA Forward and Backward
              3.  Drop Missing Values
              4. Replace Missing (or) Generic Values
      
          Missing data is always a problem in real life scenarios. Areas like machine learning and data mining face severe issues in the accuracy of their model predictions because of poor quality of data caused by missing values. In these areas, missing value treatment is a major point of focus to make their models more accurate and valid.

When and Why Is Data Missed?

           Let us consider an online survey for a product. Many a times, people do not share all the information related to them. Few people share their experience, but not how long they are using the product; few people share how long they are using the product, their experience but not their contact information. Thus, in some or the other way a part of data is always missing, and this is very common in real time.
Let us now see how we can handle missing values (say NA or NaN) using Pandas.

# import the pandas library
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df)
Its output is as follows −

         one        two      three
a   0.077988   0.476149   0.965836
b        NaN        NaN        NaN
c  -0.390208  -0.551605  -2.301950
d        NaN        NaN        NaN
e  -2.000303  -0.788201   1.510072
f  -0.930230  -0.670473   1.146615
g        NaN        NaN        NaN
h   0.085100   0.532791   0.887415
Using reindexing, we have created a DataFrame with missing values. In the output, NaN means Not a Number.

Check for Missing Values

          To make detecting missing values easier (and across different array dtypes), Pandas provides the isnone() and notnone() functions, which are also methods on Series and DataFrame objects
Example 1

import pandas as pd
import numpy as np
 
df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df['one'].isnone())
Its output is as follows −

a  false
b  true
c  false
d  true
e  false
f  false
g  true
h  false
Name: one, dtype: bool
Example 2

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df['one'].notnone())
Its output is as follows −

a  true
b  false
c  true
d  false
e  true
f  true
g  false
h  true
Name: one, dtype: bool

Calculations with Missing Data

  • When summing data, NA will be treated as Zero
  • If the data are all NA, then the result will be NA
Example 1

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df['one'].sum())
Its output is as follows −
2.02357685917

Example 2
import pandas as pd
import numpy as np

df = pd.DataFrame(index=[0,1,2,3,4,5],columns=['one','two'])
print(df['one'].sum())
Its output is as follows −
nan

Cleaning / Filling Missing Data

          Pandas provides various methods for cleaning the missing values. The fillna function can “fill in” NA values with non-none data in a couple of ways, which we have illustrated in the following sections.

1. Replace NaN with a Scalar Value

The following program shows how you can replace "NaN" with "0".

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(3, 3), index=['a', 'c', 'e'],columns=['one',
'two', 'three'])

df = df.reindex(['a', 'b', 'c'])

print(df)
print ("NaN replaced with '0':")
print(df.fillna(0))
Its output is as follows −

         one        two     three
a  -0.576991  -0.741695  0.553172
b        NaN        NaN       NaN
c   0.744328  -1.735166  1.749580

NaN replaced with '0':
         one        two     three
a  -0.576991  -0.741695  0.553172
b   0.000000   0.000000  0.000000
c   0.744328  -1.735166  1.749580
Here, we are filling with value zero; instead we can also fill with any other value.

2. Fill NA Forward and Backward

Using the concepts of filling discussed in the ReIndexing Chapter we will fill the missing values.
  • pad/fill
  • Fill methods Forward
  • bfill/backfill
  • Fill methods Backward
Example 1: Using pad/fill method

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df.fillna(method='pad'))
Its output is as follows −

         one        two      three
a   0.077988   0.476149   0.965836
b   0.077988   0.476149   0.965836
c  -0.390208  -0.551605  -2.301950
d  -0.390208  -0.551605  -2.301950
e  -2.000303  -0.788201   1.510072
f  -0.930230  -0.670473   1.146615
g  -0.930230  -0.670473   1.146615
h   0.085100   0.532791   0.887415
Example 2: Using the backfill method

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df.fillna(method='backfill'))
Its output is as follows −

         one        two      three
a   0.077988   0.476149   0.965836
b  -0.390208  -0.551605  -2.301950
c  -0.390208  -0.551605  -2.301950
d  -2.000303  -0.788201   1.510072
e  -2.000303  -0.788201   1.510072
f  -0.930230  -0.670473   1.146615
g   0.085100   0.532791   0.887415
h   0.085100   0.532791   0.887415
3. Drop Missing Values
         If you want to simply exclude the missing values, then use the dropna function along with the axis argument. By default, axis=0, i.e., along the row, which means that if any value within a row is NA then the whole row is excluded.
Example 1

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
print(df.dropna())
Its output is as follows −

         one        two      three
a   0.077988   0.476149   0.965836
c  -0.390208  -0.551605  -2.301950
e  -2.000303  -0.788201   1.510072
f  -0.930230  -0.670473   1.146615
h   0.085100   0.532791   0.887415
Example 2
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
print(df.dropna(axis=1))
Its output is as follows −
Empty DataFrame

Columns: [ ]

Index: [a, b, c, d, e, f, g, h]

4. Replace Missing (or) Generic Values

          Many times, we have to replace a generic value with some specific value. We can achieve this by applying the replacement method.
Replacing NA with a scalar value is the equivalent behavior of the fillna() function.
Example 1
import pandas as pd
import numpy as np

df = pd.DataFrame({'one':[10,20,30,40,50,2000], 'two':[1000,0,30,40,50,60]})

print(df.replace({1000:10,2000:60}))
Its output is as follows −

   one  two
0   10   10
1   20    0
2   30   30
3   40   40
4   50   50
5   60   60
Example 2
import pandas as pd
import numpy as np

df = pd.DataFrame({'one':[10,20,30,40,50,2000], 'two':[1000,0,30,40,50,60]})
print(df.replace({1000:10,2000:60}))
Its output is as follows −

   one  two
0   10   10
1   20    0
2   30   30
3   40   40
4   50   50
5   60   60
I hope you enjoyed reading this article and finally, you came to know about Python Pandas - Missing Data.
   
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