Как объединить фреймы данных pandas по столбцу: руководство

Когда вам нужно объединить две таблицы по столбцу, вы можете использовать функцию merge из библиотеки Pandas. Вот как это сделать:

import pandas as pd

# Создание первого DataFrame
data1 = {'A': [1, 2, 3],
         'B': [4, 5, 6]}
df1 = pd.DataFrame(data1)

# Создание второго DataFrame
data2 = {'A': [3, 4, 5],
         'C': [7, 8, 9]}
df2 = pd.DataFrame(data2)

# Объединение по столбцу 'A'
merged_df = pd.merge(df1, df2, on='A')

print(merged_df)

Вышеуказанный код создаст два DataFrame: df1 и df2. Затем эти две таблицы объединяются по столбцу 'A' с помощью функции merge. Результат будет содержать только строки, где значения столбца 'A' совпадают в обоих таблицах.

Introduction

When working with data in Python, it is common to have multiple data frames that need to be combined or merged together based on a specific column. The pandas merge function provides a convenient way to merge data frames by column. This article will provide an overview of the pandas merge function and its purpose, explain how it works, discuss the different types of merge operations available, and provide a step-by-step guide on merging data frames based on a specific column. Additionally, we will illustrate different examples of merging data frames by column using code examples.

What is pandas merge

The pandas merge function is a powerful tool that allows you to combine data frames based on one or more common columns. The result is a new data frame that contains all the columns from both data frames, with rows matched based on the values in the specified columns. This is especially useful when you have data sets with related information that you want to merge together.

Types of merge

There are several types of merge operations available in pandas:

• Inner merge: This is the most common type of merge operation. It returns only the rows that have matching values in both data frames based on the specified column(s). Rows with non-matching values are excluded.
• Left merge: This type of merge operation returns all the rows from the left data frame and the matching rows from the right data frame based on the specified column(s). If there is no match, the result will contain NaN values for the columns from the right data frame.
• Right merge: This is the opposite of left merge. It returns all the rows from the right data frame and the matching rows from the left data frame based on the specified column(s). If there is no match, the result will contain NaN values for the columns from the left data frame.
• Outer merge: This type of merge operation returns all the rows from both data frames, with NaN values for non-matching rows in the specified column(s).

How to merge data frames by column

Here is a step-by-step guide on merging data frames based on a specific column:

1. Import the pandas library:

import pandas as pd

2. Load the data frames:

# Load data frame 1
df1 = pd.read_csv('data1.csv')

# Load data frame 2
df2 = pd.read_csv('data2.csv')

3. Merge the data frames:

merged_df = pd.merge(df1, df2, on='common_column')

In the above code, 'common_column' is the column that you want to merge the data frames on. You can specify multiple columns by passing a list of column names.

Example scenarios

Let's take a look at some examples to illustrate different scenarios of merging data frames by column.

I hope this article provided a comprehensive explanation of merging data frames by column using the pandas merge function. By following the step-by-step guide and understanding the different types of merge operations available, you can efficiently merge data frames based on specific columns. Make sure to experiment with different scenarios and data sets to fully grasp the concepts. Happy coding!