Pandas groupby.sum() Function
groupby.sum()is an aggregation function in Pandas used to sum after grouping. It is usually used withgroupby()the function, which first groups the data by the values of a column, and then sums the numeric columns in each group.
In actual data analysis,sum()it is one of the most commonly used aggregation functions. For example, calculating the total salary for each department, total sales for each region, total quantity for each category, etc.
Basic Syntax and Parameters
sum()is a member function of the GroupBy object. You need to first usegroupby()to group the data, and then call it.
Syntax Format
GroupBy.sum(numeric_only=False, min_count=-1, engine=None, engine_kwargs=None)
Parameter Description
| Parameter | Type | Description | Default Value |
|---|---|---|---|
| numeric_only | bool | If True, only numeric columns are summed; if False, it attempts to sum all columns. | False |
| min_count | int | The minimum number of valid values required for summation. If there are fewer valid values than this number in a group, NaN is returned. | -1 |
| engine | str | Specify the computation engine, which can be 'cython' or 'numba'. None means Pandas automatically selects it. | None |
| engine_kwargs | dict | A dictionary of additional parameters passed to the underlying engine. | None |
Return Value
- Return Type：
SeriesorDataFrame - Description: Returns the result after grouping and summation. If applied to a single column, it returns a Series; if applied to multiple columns, it returns a DataFrame.
Examples
Through a series of examples from simple to complex, let's thoroughly mastergroupby.sum()the usage.
Example 1: Basic Usage - Group by a Single Column and Sum
The most basic usage is to group by the values in one column, and then sum another column.
Example
import pandas as pd
# Create sales data DataFrame
# Simulate a table containing region, product, and sales
data = {
'Region': ['North China', 'East China', 'South China', 'North China', 'East China', 'South China', 'North China', 'East China'],
'product': ['A', 'B', 'C', 'B', 'A', 'C', 'A', 'B'],
'Sales amount': [1000, 2000, 1500, 1800, 2200, 1600, 1200, 2100],
'Quantity': [10, 20, 15, 18, 22, 16, 12, 21]
}
# Create DataFrame
df = pd.DataFrame(data)
print("Original Sales Data:")
print(df)
print()
# Group by "Region" and calculate total sales for each region
sales_by_region = df.groupby('Region')['Sales amount'].sum()
print(Total sales per region:)
print(sales_by_region)
print()
# Group by "Product" and calculate the total sales quantity for each product
quantity_by_product = df.groupby('product')['Quantity'].sum()
print(Total sales quantity for each product:)
print(quantity_by_product)
# Create sales data DataFrame
# Simulate a table containing region, product, and sales
data = {
'Region': ['North China', 'East China', 'South China', 'North China', 'East China', 'South China', 'North China', 'East China'],
'product': ['A', 'B', 'C', 'B', 'A', 'C', 'A', 'B'],
'Sales amount': [1000, 2000, 1500, 1800, 2200, 1600, 1200, 2100],
'Quantity': [10, 20, 15, 18, 22, 16, 12, 21]
}
# Create DataFrame
df = pd.DataFrame(data)
print("Original Sales Data:")
print(df)
print()
# Group by "Region" and calculate total sales for each region
sales_by_region = df.groupby('Region')['Sales amount'].sum()
print(Total sales per region:)
print(sales_by_region)
print()
# Group by "Product" and calculate the total sales quantity for each product
quantity_by_product = df.groupby('product')['Quantity'].sum()
print(Total sales quantity for each product:)
print(quantity_by_product)
Output:
原始销售数据： 地区 产品 销售额 数量 0 华北 A 1000 10 1 华东 B 2000 20 2 华南 C 1500 15 3 华北 B 1800 18 4 华东 A 2200 22 5 华南 C 1600 16 6 华北 A 1200 12 7 华东 B 2100 21 每个地区的总销售额： 地区 华东 7100 华南 3100 华北 4000 dtype: int64 每个产品的总销售数量： 产品 A 44 B 59 C 31 dtype: int64
Code Explanation:
df.groupby('地区')['销售额']Indicates that the data is first grouped by the 'Region' column, and then the 'Sales amount' column is selected for operation..sum()Sum the sales in each group.- The returned result is a Series, and the index is the values of the grouping column (i.e., the names of the regions).
Example 2: Group by Multiple Columns and Sum
You can group by multiple columns at the same time, and then sum the numeric columns.
Example
import pandas as pd
# Create sales data
data = {
'Region': ['North China', 'East China', 'South China', 'North China', 'East China', 'South China', 'North China', 'East China'],
'product': ['A', 'B', 'C', 'B', 'A', 'C', 'A', 'B'],
'Sales amount': [1000, 2000, 1500, 1800, 2200, 1600, 1200, 2100],
'Quantity': [10, 20, 15, 18, 22, 16, 12, 21]
}
df = pd.DataFrame(data)
print("Original Sales Data:")
print(df)
print()
# Group by 'Region' and 'Product' columns, calculate total sales and total quantity
# as_index=False means the grouping column is kept as a regular column in the result.
grouped = df.groupby(['Region', 'product'], as_index=False).sum()
print("Total after grouping by region and product:")
print(grouped)
print()
# Alternatively, you can sum all numeric columns without specifying columns
grouped_all = df.groupby(['Region', 'product']).sum()
print("Sum all numeric columns (returns a DataFrame):")
print(grouped_all)
# Create sales data
data = {
'Region': ['North China', 'East China', 'South China', 'North China', 'East China', 'South China', 'North China', 'East China'],
'product': ['A', 'B', 'C', 'B', 'A', 'C', 'A', 'B'],
'Sales amount': [1000, 2000, 1500, 1800, 2200, 1600, 1200, 2100],
'Quantity': [10, 20, 15, 18, 22, 16, 12, 21]
}
df = pd.DataFrame(data)
print("Original Sales Data:")
print(df)
print()
# Group by 'Region' and 'Product' columns, calculate total sales and total quantity
# as_index=False means the grouping column is kept as a regular column in the result.
grouped = df.groupby(['Region', 'product'], as_index=False).sum()
print("Total after grouping by region and product:")
print(grouped)
print()
# Alternatively, you can sum all numeric columns without specifying columns
grouped_all = df.groupby(['Region', 'product']).sum()
print("Sum all numeric columns (returns a DataFrame):")
print(grouped_all)
Output:
原始销售数据：
地区 产品 销售额 数量
0 华北 A 1000 10
1 华东 B 2000 20
2 华南 C 1500 15
3 华北 B 1800 18
4 华东 A 2200 22
5 华南 C 1600 16
6 华北 A 1200 12
7 华东 B 2100 21
按地区和产品分组后的总计：
地区 产品 销售额 数量
0 华东 A 2200 22
1 华东 B 4100 41
2 华南 C 3100 31
3 华北 A 2200 22
4 华北 B 1800 18
对所有数值列求和（返回DataFrame）：
销售额 数量
地区 产品
华东 A 2200 22
B 4100 41
华南 C 3100 31
华北 A 2200 22
B 1800 18
Code Explanation:
['Region', 'Product']Using a list allows grouping by multiple columns at the same time.as_index=FalseIn this case, the result is in DataFrame format, and the grouping columns are retained as ordinary columns.- When no column is specified (e.g.,
.sum()), the sum of all numeric columns is calculated.
Example 3: Using the min_count Parameter
min_countThis parameter controls the minimum number of valid values required for group summation. When the number of valid values in a group is less than this number, NaN is returned.
Example
import pandas as pd
import numpy as np
# Create data with missing values
data = {
'Department': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],
'salary': [5000, 6000, np.nan, 7000, np.nan, np.nan, 8000, 9000, 10000]
}
df = pd.DataFrame(data)
print(Employee salary data (including missing values):)
print(df)
print()
# By default, sum() ignores NaN values when summing
sum_default = df.groupby('Department')['salary'].sum()
print(Default sum (ignoring NaN):)
print(sum_default)
print()
# Set min_count=3, requiring at least 3 valid values to perform summation
sum_min_count = df.groupby('Department')['salary'].sum(min_count=3)
print(Sum result when min_count=3:)
print(sum_min_count)
print()
# Set min_count=2
sum_min_count2 = df.groupby('Department')['salary'].sum(min_count=2)
print(Sum result when min_count=2:)
print(sum_min_count2)
import numpy as np
# Create data with missing values
data = {
'Department': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],
'salary': [5000, 6000, np.nan, 7000, np.nan, np.nan, 8000, 9000, 10000]
}
df = pd.DataFrame(data)
print(Employee salary data (including missing values):)
print(df)
print()
# By default, sum() ignores NaN values when summing
sum_default = df.groupby('Department')['salary'].sum()
print(Default sum (ignoring NaN):)
print(sum_default)
print()
# Set min_count=3, requiring at least 3 valid values to perform summation
sum_min_count = df.groupby('Department')['salary'].sum(min_count=3)
print(Sum result when min_count=3:)
print(sum_min_count)
print()
# Set min_count=2
sum_min_count2 = df.groupby('Department')['salary'].sum(min_count=2)
print(Sum result when min_count=2:)
print(sum_min_count2)
Output:
员工工资数据（包含缺失值）： 部门 工资 0 A 5000.0 1 A 6000.0 2 A NaN 3 B 7000.0 4 B NaN 5 B NaN 6 C 8000.0 7 C 9000.0 8 C 10000.0 默认求和（忽略NaN）： 部门 A 11000.0 B 7000.0 C 27000.0 dtype: float64 min_count=3 时的求和结果： 部门 A 11000.0 B NaN C 27000.0 dtype: float64 min_count=2 时的求和结果： 部门 A 11000.0 B 7000.0 C 27000.0 dtype: float64
Code Explanation:
- By default,
sum()it ignores NaN values and only sums valid values. min_count=3It means that each group must have at least 3 valid values before the sum is calculated; otherwise, NaN is returned.- Department A has 2 valid values (5000 and 6000), which does not meet min_count=3, so it returns NaN; Department C has 3 valid values, so it can be summed normally.
Notes:
sum()By default, NaN values are ignored when summing, which is consistent with our daily understanding of "null" handling. If you need to treat NaN as 0 before summing, you can first usefillna(0)the method to fill missing values.
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