Pandas counting and summing specific conditions

Pandas counting and summing specific conditions

You can count and sum specific conditions in a Pandas DataFrame using the sum() and count() methods in combination with boolean indexing. Here's how you can do it:

Let's assume you have a DataFrame called df:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
        'Age': [25, 30, 22, 28, 25],
        'Gender': ['Female', 'Male', 'Male', 'Male', 'Female'],
        'Salary': [50000, 60000, 45000, 55000, 48000]}

df = pd.DataFrame(data)

Now, let's say you want to count and sum specific conditions based on certain criteria:

Counting based on a condition:

For example, to count the number of rows where the 'Gender' column is 'Male', you can use:

male_count = (df['Gender'] == 'Male').sum()
print("Number of males:", male_count)

Summing based on a condition:

For example, to sum the 'Salary' column for rows where the 'Age' is greater than 25, you can use:

salary_sum_above_25 = df.loc[df['Age'] > 25, 'Salary'].sum()
print("Total salary for people above 25:", salary_sum_above_25)

You can replace the conditions and column names with your specific criteria to count and sum based on your needs.

Examples

  1. "Pandas count rows with specific condition" Description: How to count the number of rows in a DataFrame that satisfy a particular condition using Pandas. Code:

    import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10]}

df = pd.DataFrame(data)

# Count rows where column 'A' is greater than 3
count_condition = (df['A'] > 3).sum()
print("Number of rows where column 'A' is greater than 3:", count_condition)

    This code snippet demonstrates how to count the number of rows in a DataFrame where a specific condition (here, 'A' > 3) is satisfied.

  2. "Pandas sum values with specific condition" Description: How to sum values in a DataFrame column based on a specific condition using Pandas. Code:

    import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10]}

df = pd.DataFrame(data)

# Sum values in column 'B' where column 'A' is greater than 3
sum_condition = df.loc[df['A'] > 3, 'B'].sum()
print("Sum of values in column 'B' where column 'A' is greater than 3:", sum_condition)

    This code calculates the sum of values in column 'B' of a DataFrame where the corresponding values in column 'A' are greater than 3.

  3. "Pandas count rows with multiple conditions" Description: How to count the number of rows in a DataFrame that satisfy multiple conditions using Pandas. Code:

    import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10]}

df = pd.DataFrame(data)

# Count rows where both 'A' is greater than 2 and 'B' is less than 9
count_condition = ((df['A'] > 2) & (df['B'] < 9)).sum()
print("Number of rows where 'A' > 2 and 'B' < 9:", count_condition)

    This code snippet demonstrates counting the number of rows in a DataFrame that satisfy multiple conditions ('A' > 2 and 'B' < 9).

  4. "Pandas sum values with multiple conditions" Description: How to sum values in a DataFrame column based on multiple conditions using Pandas. Code:

    import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10]}

df = pd.DataFrame(data)

# Sum values in column 'B' where both 'A' is greater than 2 and 'B' is less than 9
sum_condition = df.loc[(df['A'] > 2) & (df['B'] < 9), 'B'].sum()
print("Sum of values in column 'B' where 'A' > 2 and 'B' < 9:", sum_condition)

    This code calculates the sum of values in column 'B' of a DataFrame where both 'A' > 2 and 'B' < 9.

  5. "Pandas count rows with null values" Description: How to count the number of rows in a DataFrame that contain null (NaN) values using Pandas. Code:

    import pandas as pd

# Sample DataFrame with null values
data = {'A': [1, 2, None, 4, 5],
        'B': [6, None, 8, 9, 10]}

df = pd.DataFrame(data)

# Count rows with null values
count_null = df.isnull().any(axis=1).sum()
print("Number of rows with null values:", count_null)

    This code snippet counts the number of rows in a DataFrame that contain null (NaN) values in any column.

  6. "Pandas sum values excluding null values" Description: How to sum values in a DataFrame column excluding null (NaN) values using Pandas. Code:

    import pandas as pd

# Sample DataFrame with null values
data = {'A': [1, 2, None, 4, 5],
        'B': [6, None, 8, 9, 10]}

df = pd.DataFrame(data)

# Sum values in column 'A' excluding null values
sum_excluding_null = df['A'].sum(skipna=True)
print("Sum of values in column 'A' excluding null values:", sum_excluding_null)

    This code calculates the sum of values in column 'A' of a DataFrame, excluding null (NaN) values.

  7. "Pandas count rows meeting multiple conditions with null values" Description: How to count the number of rows in a DataFrame that satisfy multiple conditions, including handling null (NaN) values using Pandas. Code:

    import pandas as pd

# Sample DataFrame with null values
data = {'A': [1, 2, None, 4, 5],
        'B': [6, None, 8, 9, 10]}

df = pd.DataFrame(data)

# Count rows where 'A' is greater than 2 and 'B' is not null
count_condition = ((df['A'] > 2) & (~df['B'].isnull())).sum()
print("Number of rows where 'A' > 2 and 'B' is not null:", count_condition)

    This code snippet counts the number of rows in a DataFrame that satisfy multiple conditions ('A' > 2 and 'B' is not null), including handling null (NaN) values.

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