Write a program in Python to count the records based on the designation in a given DataFrame

To count records based on designation in a pandas DataFrame, we use the groupby() method combined with count(). This groups rows by designation and counts occurrences in each group.

Creating the DataFrame

Let's start by creating a sample DataFrame with employee data ?

import pandas as pd

data = {
    'Id': [1, 2, 3, 4, 5],
    'Designation': ['architect', 'scientist', 'programmer', 'scientist', 'programmer']
}
df = pd.DataFrame(data)
print("DataFrame is:")
print(df)

DataFrame is:
   Id Designation
0   1   architect
1   2   scientist
2   3  programmer
3   4   scientist
4   5  programmer

Counting Records by Designation

Use groupby() to group records by designation, then count() to get the total count for each group ?

import pandas as pd

data = {
    'Id': [1, 2, 3, 4, 5],
    'Designation': ['architect', 'scientist', 'programmer', 'scientist', 'programmer']
}
df = pd.DataFrame(data)

print("Count of records by designation:")
result = df.groupby(['Designation']).count()
print(result)

Count of records by designation:
            Id
Designation   
architect    1
programmer   2
scientist    2

Alternative Approaches

You can also use size() instead of count() for counting ?

import pandas as pd

data = {
    'Id': [1, 2, 3, 4, 5],
    'Designation': ['architect', 'scientist', 'programmer', 'scientist', 'programmer']
}
df = pd.DataFrame(data)

print("Using size():")
result = df.groupby('Designation').size()
print(result)

Using size():
Designation
architect     1
programmer    2
scientist     2
dtype: int64

Difference Between count() and size()

Conclusion

Use groupby(['Designation']).count() to count non-null records by designation. Use size() if you want to include NaN values and get a simpler Series output.