﻿ Pandas: Find integer index of rows with missing data in pandas dataframe - w3resource # Pandas: Find integer index of rows with missing data in pandas dataframe

## Pandas Indexing: Exercise-24 with Solution

Write a Pandas program to find integer index of rows with missing data in a given dataframe.

Sample Solution:

Python Code :

``````import pandas as pd
import numpy as np
df = pd.DataFrame({
'school_code': ['s001','s002','s003','s001','s002','s004'],
'class': ['V', 'V', 'VI', 'VI', 'V', 'VI'],
'name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Gino Mcneill', 'David Parkes'],
'date_of_birth': ['15/05/2002','17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],
'weight': [35, None, 33, 30, 31, None]},
index = ['t1', 't2', 't3', 't4', 't5', 't6'])
print("Original DataFrame:")
print(df)
index = df['weight'].index[df['weight'].apply(np.isnan)]
df_index = df.index.values.tolist()
print("\nInteger index of rows with missing data in 'weight' column of the said dataframe:")
print([df_index.index(i) for i in index])
``````

Sample Output:

```Original DataFrame:
school_code class            name date_of_birth  weight
t1        s001     V  Alberto Franco    15/05/2002    35.0
t2        s002     V    Gino Mcneill    17/05/2002     NaN
t3        s003    VI     Ryan Parkes    16/02/1999    33.0
t4        s001    VI    Eesha Hinton    25/09/1998    30.0
t5        s002     V    Gino Mcneill    11/05/2002    31.0
t6        s004    VI    David Parkes    15/09/1997     NaN

Integer index of rows with missing data in 'weight' column of the said dataframe:
[1, 5]
```

Python Code Editor:

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## Python: Tips of the Day

For-else construct useful when searched for something and find it:

```# For example assume that I need to search through a list and process each item until a flag item is found and
# then stop processing. If the flag item is missing then an exception needs to be raised.

for i in mylist:
if i == theflag:
break
process(i)
else:
raise ValueError("List argument missing terminal flag.")
```