How to Count the Frequency of Rows Based on Multiple Columns in a Pandas DataFrame?

Get a Frequency Count Based on Multiple Dataframe Columns

To find the frequency of rows that appear multiple times in a dataframe, you can utilize the groupby operation with either size or count functions. Let's demonstrate this with an example dataframe:

import pandas as pd

# Sample dataframe
data = {'Group': ['Short', 'Short', 'Moderate', 'Moderate', 'Tall'], 'Size': ['Small', 'Small', 'Medium', 'Small', 'Large']}
df = pd.DataFrame(data)

Option 1: Using groupby and size

dfg = df.groupby(['Group', 'Size']).size()
print(dfg)

Output:

Group     Size
Moderate  Medium    1
          Small     1
Short     Small     2
Tall      Large     1
dtype: int64

Option 2: Using groupby, size, and reset_index

dfg = df.groupby(['Group', 'Size']).size().reset_index(name='Time')
print(dfg)

Output:

      Group    Size  Time
0  Moderate  Medium     1
1  Moderate   Small     1
2     Short   Small     2
3      Tall   Large     1

Option 3: Using groupby, size, and as_index

dfg = df.groupby(['Group', 'Size'], as_index=False).size()
print(dfg)

Output:

      Group    Size  Time
0  Moderate  Medium     1
1  Moderate   Small     1
2     Short   Small     2
3      Tall   Large     1

Each option returns a dataframe with Group and Size columns, indicating the specific row combinations that appear in the original dataframe. An additional Time column shows the frequency count for each combination.

The above is the detailed content of How to Count the Frequency of Rows Based on Multiple Columns in a Pandas DataFrame?. For more information, please follow other related articles on the PHP Chinese website!