Learn to use commonly used pandas functions to easily process large-scale data

Master the common functions of the pandas library and easily process big data. Specific code examples are needed

With the advent of the big data era, data processing has become more and more important. As one of the most commonly used data processing libraries in Python, the pandas library is loved by the majority of data analysts and scientists for its powerful functions and flexible processing methods. This article will introduce some commonly used functions in the pandas library and provide specific code examples to help readers get started quickly and process big data easily.

1. Data reading and writing

pandas provides a variety of ways to read data, the most commonly used is to read csv files. Use the pandas.read_csv() function to directly read the csv file into a DataFrame object.

import pandas as pd

# 读取csv文件
data = pd.read_csv('data.csv')

Similarly, we can use the pandas.DataFrame.to_csv() function to write the DataFrame object to a csv file.

# 将DataFrame对象写入csv文件
data.to_csv('result.csv', index=False)

2. View data

When dealing with big data, you first need to understand the overall situation of the data. Pandas provides several commonly used functions that can help us view the first few rows, last few rows, and overall statistical summary information of the data.

• head() function can view the first few rows of the DataFrame, and the first 5 rows are displayed by default.

# 查看前5行数据
print(data.head())

• tail()The function can view the last few rows of the DataFrame, and the last 5 rows are displayed by default.

# 查看后5行数据
print(data.tail())

• describe()The function can view the statistical summary information of the DataFrame, including count, average, standard deviation, minimum value, maximum value, etc.

# 查看统计摘要信息
print(data.describe())

3. Data screening and filtering

When processing big data, we often need to screen and filter the data based on specific conditions. Pandas provides several commonly used functions to help us achieve this function.

• Use the loc[] function to filter data by tags.

# 筛选某一列中值大于10的数据
filtered_data = data.loc[data['column'] > 10]

• Use the isin() function to filter based on the values ​​in a list.

# 筛选某一列中值在列表[1,2,3]中的数据
filtered_data = data[data['column'].isin([1, 2, 3])]

• Use the query() function to filter based on conditional expressions.

# 筛选某一列中值大于10且小于20的数据
filtered_data = data.query('10 < column < 20')

4. Data sorting and rearrangement

When dealing with big data, data sorting and rearrangement are often essential operations. Pandas provides multiple functions to help us achieve this function.

• Use the sort_values() function to sort the data according to the specified column.

# 按照某一列的值对数据进行升序排序
sorted_data = data.sort_values(by='column', ascending=True)

• Use the sort_index() function to sort the data according to the index.

# 按照索引对数据进行升序排序
sorted_data = data.sort_index(ascending=True)

5. Data Grouping and Aggregation

When processing big data, it is often necessary to group data according to certain conditions and perform aggregation calculations on each group . Pandas provides multiple functions to help us accomplish this task.

• Use the groupby() function to group by a certain column.

# 根据某一列进行分组
grouped_data = data.groupby('column')

• Use the agg() function to perform aggregation calculations on grouped data.

# 对分组后的数据进行求和操作
sum_data = grouped_data.agg({'column': 'sum'})

6. Data Merger and Connection

When dealing with big data, it is often necessary to merge or join multiple data sets together. Pandas provides multiple functions to help us achieve this function.

• Use the merge() function to merge two data sets together based on specified columns.

# 按照某一列进行合并
merged_data = pd.merge(data1, data2, on='column')

• Use the concat() function to join multiple data sets together in rows or columns.

# 按行连接两个数据集
concatenated_data = pd.concat([data1, data2], axis=0)

The above introduces some commonly used functions in the pandas library and specific code examples. I hope it will be helpful to readers when processing big data. Of course, the pandas library has more powerful functions, and you can further explore official documents and other materials when it comes to more complex scenarios. I wish readers can easily handle big data and achieve better analysis results!

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