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Pandas agg sum

Pandas agg sum

Pandas is a powerful Python library for data manipulation and analysis. It provides numerous functions to perform aggregations, one of which is the sum() function. In this article, we will explore the agg() function in detail, focusing on its use with the sum() operation to perform summations over DataFrame columns.

Introduction to Pandas DataFrame

A DataFrame is a two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Before diving into examples, let’s first understand how to create a DataFrame.

Example 1: Creating a DataFrame

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Age': [25, 30, 35, 40, 45],
    'Income': [50000, 54000, 70000, 68000, 62000]
}
df = pd.DataFrame(data)
print(df)

Output:

Pandas agg sum

Basic Summation with sum()

The sum() function is used to calculate the sum of array elements over a specified axis.

Example 2: Summing a Single Column

import pandas as pd

df = pd.DataFrame({
    'Sales': [300, 450, 500, 650, 700],
    'pandasdataframe.com': [1, 2, 3, 4, 5]
})
total_sales = df['Sales'].sum()
print(total_sales)

Output:

Pandas agg sum

Example 3: Summing Multiple Columns

import pandas as pd

df = pd.DataFrame({
    'Sales': [300, 450, 500, 650, 700],
    'Expenses': [150, 200, 240, 300, 350],
    'pandasdataframe.com': [1, 2, 3, 4, 5]
})
total = df[['Sales', 'Expenses']].sum()
print(total)

Output:

Pandas agg sum

Using agg() for Summation

The agg() function allows more flexibility. It can apply different aggregation functions to different columns, or multiple aggregation functions to each column.

Example 4: Using agg() with sum()

import pandas as pd

df = pd.DataFrame({
    'Sales': [300, 450, 500, 650, 700],
    'Expenses': [150, 200, 240, 300, 350],
    'pandasdataframe.com': [1, 2, 3, 4, 5]
})
result = df.agg({'Sales': 'sum', 'Expenses': 'sum'})
print(result)

Output:

Pandas agg sum

Example 5: Multiple Aggregations on a Single Column

import pandas as pd

df = pd.DataFrame({
    'Sales': [300, 450, 500, 650, 700],
    'pandasdataframe.com': [1, 2, 3, 4, 5]
})
result = df['Sales'].agg(['sum', 'mean'])
print(result)

Output:

Pandas agg sum

Example 6: Different Aggregations for Different Columns

import pandas as pd

df = pd.DataFrame({
    'Sales': [300, 450, 500, 650, 700],
    'Expenses': [150, 200, 240, 300, 350],
    'pandasdataframe.com': [1, 2, 3, 4, 5]
})
result = df.agg({'Sales': ['sum', 'mean'], 'Expenses': ['sum', 'max']})
print(result)

Output:

Pandas agg sum

Advanced Usage of agg() and sum()

Example 7: Using Custom Functions with agg()

import pandas as pd

def increment_sum(x):
    return x.sum() + 100

df = pd.DataFrame({
    'Sales': [300, 450, 500, 650, 700],
    'pandasdataframe.com': [1, 2, 3, 4, 5]
})
result = df['Sales'].agg(increment_sum)
print(result)

Output:

Pandas agg sum

Example 8: Aggregating Over Rows

import pandas as pd

df = pd.DataFrame({
    'Sales': [300, 450, 500],
    'Expenses': [150, 200, 240],
    'pandasdataframe.com': [1, 2, 3]
})
result = df.agg('sum', axis=1)
print(result)

Output:

Pandas agg sum

Example 9: Using agg() with Lambda Functions

import pandas as pd

df = pd.DataFrame({
    'Sales': [300, 450, 500, 650, 700],
    'Expenses': [150, 200, 240, 300, 350],
    'pandasdataframe.com': [1, 2, 3, 4, 5]
})
result = df.agg({'Sales': lambda x: x.sum() + 500})
print(result)

Output:

Pandas agg sum

Example 10: Summation with Condition

import pandas as pd

df = pd.DataFrame({
    'Sales': [300, 450, 500, 650, 700],
    'Region': ['East', 'West', 'East', 'West', 'East'],
    'pandasdataframe.com': [1, 2, 3, 4, 5]
})
sum_east = df[df['Region'] == 'East']['Sales'].sum()
print(sum_east)

Output:

Pandas agg sum

Pandas agg sum conclusion

In this article, we have explored how to use the agg() function in Pandas to perform summations and other aggregations. We’ve seen how to apply it to single columns, multiple columns, and even with custom functions. The flexibility of agg() makes it a powerful tool for data analysis, allowing for complex aggregations that are tailored to specific needs.

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