Top 10 Most Common Pandas Functions with Code Examples

Pandas is a powerful Python library for data analysis, offering numerous functions to efficiently process data. Below are the ten most frequently used Pandas functions, each with a brief description and example code.

1. read_csv() reads data from a CSV file into a DataFrame.

import pandas as pd
# 从CSV文件读取数据
df = pd.read_csv('data.csv')
print("从CSV文件读取的数据:")
print(df)

2. head() displays the first n rows of a DataFrame (default 5).

# 显示前5行
print("显示前5行:")
print(df.head())

3. tail() displays the last n rows of a DataFrame (default 5).

# 显示后5行
print("显示后5行:")
print(df.tail())

4. info() provides basic information about a DataFrame, including column names, data types, and non‑null counts.

# 获取DataFrame的基本信息
print("获取DataFrame的基本信息:")
print(df.info())

5. describe() generates descriptive statistics such as count, mean, std, min, and max.

# 生成描述性统计信息
print("生成描述性统计信息:")
print(df.describe())

6. dropna() removes rows or columns containing missing values.

# 删除包含缺失值的行
df_clean = df.dropna()
print("删除包含缺失值的行后的DataFrame:")
print(df_clean)

7. fillna() fills missing values with a specified value.

# 填充缺失值
df_filled = df.fillna(0)
print("填充缺失值后的DataFrame:")
print(df_filled)

8. groupby() groups data by a specified key and performs aggregation.

# 按某一列分组并计算均值
grouped_df = df.groupby('column_name').mean()
print("按某一列分组并计算均值后的DataFrame:")
print(grouped_df)

9. merge() merges two DataFrames on a common column.

# 创建另一个DataFrame
df2 = pd.DataFrame({
    'column_name': ['A', 'B', 'C'],
    'value': [1, 2, 3]
})
# 合并两个DataFrame
merged_df = pd.merge(df, df2, on='column_name')
print("合并两个DataFrame后的结果:")
print(merged_df)

10. to_csv() exports a DataFrame to a CSV file.

# 将DataFrame导出为CSV文件
df.to_csv('output.csv', index=False)
print("已导出为CSV文件")

These ten functions cover data loading, preprocessing, analysis, and export, making them essential tools for effective data analysis with Pandas.