How to use the pandas module for data analysis in Python 2.x

How to use the pandas module for data analysis in Python 2.x

Overview:
In the process of data analysis and data processing, pandas is a very powerful and commonly used Python library. It provides data structures and data analysis tools for fast and efficient data processing and analysis. This article will introduce how to use pandas for data analysis in Python 2.x and provide readers with some code examples.

Install pandas:
Before starting, you first need to install the pandas library. You can enter the following command through the terminal or command prompt to install:

pip install pandas

Data structure:
pandas provides two main data structures: 1) Series; 2) DataFrame.

Series is an indexed one-dimensional array structure, similar to a column in Excel. Code example:

import pandas as pd

# 创建一个Series对象
data = pd.Series([1, 3, 5, np.nan, 6, 8])

print(data)

Output result:

0    1.0
1    3.0
2    5.0
3    NaN
4    6.0
5    8.0
dtype: float64

DataFrame is a two-dimensional table structure, similar to a table in Excel. Code example:

import pandas as pd
import numpy as np

# 创建一个DataFrame对象
data = pd.DataFrame({
    "A": [1, 2, 3, 4],
    "B": pd.Timestamp('20130102'),
    "C": pd.Series(1, index=list(range(4)), dtype='float32'),
    "D": np.array([3] * 4, dtype='int32'),
    "E": pd.Categorical(["test", "train", "test", "train"]),
    "F": 'foo'
})

print(data)

Output results:

   A          B    C  D      E    F
0  1 2013-01-02  1.0  3   test  foo
1  2 2013-01-02  1.0  3  train  foo
2  3 2013-01-02  1.0  3   test  foo
3  4 2013-01-02  1.0  3  train  foo

Data reading and writing:
pandas can read and write multiple data formats, including CSV files, Excel files, SQL Database etc.

CSV file reading example:

import pandas as pd

# 从CSV文件中读取数据
data = pd.read_csv('data.csv')

print(data.head())

Excel file reading example:

import pandas as pd

# 从Excel文件中读取数据
data = pd.read_excel('data.xlsx')

print(data.head())

Data analysis and processing:
pandas provides many powerful functions and methods , for data analysis and processing.

Data statistical analysis example:

import pandas as pd

# 读取数据
data = pd.read_csv('data.csv')

# 统计描述性统计信息
print(data.describe())

# 计算各列之间的相关系数
print(data.corr())

Data filtering and sorting example:

import pandas as pd

# 读取数据
data = pd.read_csv('data.csv')

# 筛选出满足条件的数据
filtered_data = data[data['age'] > 30]

# 按照某列进行排序
sorted_data = data.sort_values('age')

print(filtered_data.head())
print(sorted_data.head())

Data grouping and aggregation example:

import pandas as pd

# 读取数据
data = pd.read_csv('data.csv')

# 按照某一列进行分组
grouped_data = data.groupby('gender')

# 计算每组的平均值
mean_data = grouped_data.mean()

print(mean_data)

Data is written to CSV or Excel file example:

import pandas as pd

# 读取数据
data = pd.read_csv('data.csv')

# 将数据写入到CSV文件中
data.to_csv('output.csv', index=False)

# 将数据写入到Excel文件中
data.to_excel('output.xlsx', index=False)

Summary:
pandas is a commonly used data analysis library in Python 2.x. This article introduces the installation method of pandas and common data structures, data reading and writing methods, as well as common methods of data analysis and processing. Readers can flexibly use pandas for data analysis and processing according to their own needs.

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