Python的pandas库基础知识超详细教程

更新时间：2025年09月26日 14:42:23 作者：小星袁

Pandas是Python数据处理核心库,提供Series和DataFrame结构,支持CSV/Excel/SQL等数据源导入及清洗、合并、统计等功能,与Matplotlib集成实现可视化,广泛应用于金融、统计等领域,本文给大家介绍Python的pandas库基础知识超详细教程,感兴趣的朋友跟随小编一起看看吧

Pandas是Python中用于数据处理和分析的核心库，提供了快速、灵活且明确的数据结构，主要包括一维的Series和二维的DataFrame。它支持从CSV、Excel、SQL等多种数据源导入数据，并具备数据清洗、合并、重塑、分组统计、时间序列分析等功能。Pandas还易于与其他Python数据分析库集成，是金融、统计、社会科学和工程等领域进行数据分析和处理的强大工具。

一、配置环境

在命令行中运行以下命令：

pip show pandas

如果为以下内容，则表示未安装pandas库

要安装Pandas库，你可以使用Python的包管理工具pip。在命令行界面（例如终端、命令提示符或Anaconda Prompt，取决于你的操作系统和Python安装方式）中，输入以下命令：

pip install pandas

安装成功展示图：

二、序列和数据表

2.1 初始化

Series可以存储任何数据类型，例如整数、浮点数、字符串、python对象等，每个元素都有一个索引。

import pandas as pd
A = pd.Series(data = [1, 2, 3, 4, 5], index = ["A", "B", "C", "D", "E"], name = "A1")
print(A)

2.2 获取数值

import pandas as pd
A = pd.Series(data = [1, 2, 3, 4, 5], index = ["A", "B", "C", "D", "E"], name = "A1")
print(A)
print("数值：", A.values)

2.3 获取索引

import pandas as pd
A = pd.Series(data = [1, 2, 3, 4, 5], index = ["A", "B", "C", "D", "E"], name = "A1")
print(A)
print("索引：", A.index)

2.4 索引取内容

import pandas as pd
A = pd.Series(data = [1, 2, 3, 4, 5], index = ["A", "B", "C", "D", "E"], name = "A1")
print(A)
print(A[["A", "C"]])

2.5 索引改变取值

import pandas as pd
A = pd.Series(data = [1, 2, 3, 4, 5], index = ["A", "B", "C", "D", "E"], name = "A1")
print(A)
A[["A", "C"]] = [11, 12]
print(A)

2.6 字典生成序列

import pandas as pd
A = pd.Series({"A":1, "B":2, "C":3, "D":4})
print(A)

2.7 计算取值出现次数

import pandas as pd
A = pd.Series({"A":1, "B":2, "C":3, "D":4, "E":2, "F":3})
print(A.value_counts())

2.8 数据表

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"]}
B = pd.DataFrame(A)
print(B)

2.9 数据表添加新变量

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"]}
B = pd.DataFrame(A)
print(B)
B["high"] = ["180", "183", "160", "178", "158"]
print(B)

2.10 获取列名

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B)
print("数据表列名：", B.columns)

2.11 根据列名获取数据

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B)
print(B[["name", "sex"]])

2.12 输出固定行

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B.loc[2])

2.13 输出多行

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B.loc[2 : 4])

2.14 输出指定行和列

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B.loc[2 : 4, ["name", "high"]])

2.15 输出性别为“男”的行和列

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B.loc[B.sex == "男", ["name", "sex"]])

2.16 获取指定行

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B.iloc[0 : 2])

2.17 获取指定列

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B.iloc[ : , 0 : 2])

2.18 获取指定位置数据

import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(B.iloc[0 : 2, 0 : 2])

2.19 索引转化

import numpy as np
import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
# 转换为列表
print(B.iloc[list(B.sex == "男"), 0 : 3])
# 转换为数组
print(B.iloc[np.array(B.sex == "男"), 0 : 3])

2.20 判断条件

import numpy as np
import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
print(list(B.age >= "18"))

2.21 重新赋值

import numpy as np
import pandas as pd
A = {"name": ["小米", "小华", "小魅", "小破", "小领"],
     "age": ["20", "18", "16", "23", "19"],
     "sex": ["男", "男", "女", "男", "女"],
     "high": ["180", "183", "160", "178", "158"]}
B = pd.DataFrame(A)
B.high = ["179", "186", "168", "183", "160"]
print(B)

三、数据聚合和分组运算

3.1 获取数据集

iris.csv（iris数据集、鸢尾花数据集)

3.2 读取数据集

        鸢尾花数据集（Iris Dataset），又称安德森鸢尾花卉数据集（Anderson’s Iris Data Set），是数据科学与机器学习领域中最著名的经典数据集之一。

        鸢尾花数据集可以通过多种方式获取，如Scikit-learn提供的内置数据集，以及UCI机器学习库等。获取后，可以使用Python等编程语言进行数据加载、预处理和模型训练等操作。

        鸢尾花数据集以其简洁明了的数据结构和广泛的应用场景，成为了机器学习初学者的首选案例。通过学习和实践这一数据集，初学者可以逐步掌握机器学习的基础知识和技能。

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
print(iris.head())

3.3 计算每列均值

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
print(iris.iloc[ : , 1 : 5].apply(func = np.mean, axis = 0))

3.4 计算每列的最小值

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
min = iris.iloc[ : , 1 : 5].apply(func = np.min , axis = 0)
print(min)

3.5 计算每列的最大值

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
max = iris.iloc[ : , 1 : 5].apply(func = np.max , axis = 0)
print(max)

3.6 计算每列的样本数量

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
size = iris.iloc[ : , 1 : 5].apply(func = np.size , axis = 0)
print(size)

3.7 行计算

只展示前五行

其中代码的axis=0要改成axis=1

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
data = iris.iloc[0 : 5, 1 : 5].apply(func = (np.min, np.max, np.mean, np.std, np.var) , axis = 1)
print(data)

3.8 分组计算均值

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
res = iris.drop("Id", axis = 1).groupby(by = "Species").mean()
print(res)

3.9 分组计算偏度

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
res = iris.drop("Id", axis = 1).groupby(by = "Species").skew()
print(res)

3.10 聚合运算

3.10.1 分组前

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
res = iris.drop("Id", axis = 1).agg({"SepalLengthCm" : ["min", "max", "mean"],
                                     "SepalWidthCm" : ["min", "max", "mean"],
                                     "PetalLengthCm" : ["min", "max", "mean"]})
print(res)

3.10.2 分组后

import numpy as np
import pandas as pd
iris = pd.read_csv("D:/iris.csv")
res = (iris.drop("Id", axis = 1).groupby(by = "SepalLengthCm")
       .agg({"SepalLengthCm" : ["min", "max", "mean"],
             "SepalWidthCm" : ["min"],
             "PetalLengthCm" : ["skew"]}))
print(res)

四、数据可视化

Mtplotlib是Python中一个广泛使用的绘图库，它提供了一个类似于MATLAB的绘图框架。Mtplotlib可以生成高质量的图表，这些图表可以用于数据可视化、科学研究、教育以及出版等领域。

4.1 安装matplotlib库

pip install matplotlib