Python的pandas库基础知识（超详细教学）

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）资源-CSDN文库https://download.csdn.net/download/Z0412_J0103/90215255

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

安装成功展示图：

4.2 检测matplotlib库

pip show matplotlib

4.3 箱线图

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

iris = pd.read_csv("D:/iris.csv")
iris.iloc[ : , 1 : 6].boxplot(column = ["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm"], by = "Species", figsize=(10,10))
plt.show()

4.4 散点图

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

iris = pd.read_csv("D:/iris.csv")
color = iris.Species.map({"setosa" : "blue", "versicolor" : "green", "virginica" : "red"})
iris.plot(kind = "scatter" , x = "SepalLengthCm", y = "SepalWidthCm", s = 30, c = color, figsize = (10,10))
plt.show()

4.5 六边形热力图

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

iris = pd.read_csv("D:/iris.csv")
iris.plot(kind = "hexbin" , x = "SepalLengthCm", y = "SepalWidthCm", gridsize = 15, figsize = (10,7), sharex = False)
plt.show()

4.6 折线图

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

iris = pd.read_csv("D:/iris.csv")
iris.iloc[ : , 0 : 5].plot(kind = "line", x = "Id", figsize = (12, 8))
plt.show()

上一篇文章：Python的Numpy库应用入门（超详细教程）-CSDN博客https://blog.csdn.net/Z0412_J0103/article/details/144840505下一篇文章： Python的Matplotlib库应用（超详细教程）-CSDN博客https://blog.csdn.net/Z0412_J0103/article/details/144900714

Python的pandas库基础知识（超详细教学）

一、配置环境

二、序列和数据表

2.1 初始化

2.2 获取数值

2.3 获取索引

2.4 索引取内容

2.5 索引改变取值

2.6 字典生成序列

2.7 计算取值出现次数

2.8 数据表

2.9 数据表添加新变量

2.10 获取列名

2.11 根据列名获取数据

2.12 输出固定行

2.13 输出多行

2.14 输出指定行和列

2.15 输出性别为“男”的行和列

2.16 获取指定行

2.17 获取指定列

2.18 获取指定位置数据

2.19 索引转化

2.20 判断条件

2.21 重新赋值

三、数据聚合和分组运算

3.1 获取数据集

3.2 读取数据集

3.3 计算每列均值

3.4 计算每列的最小值

3.5 计算每列的最大值

3.6 计算每列的样本数量

3.7 行计算

3.8 分组计算均值

3.9 分组计算偏度

3.10 聚合运算

3.10.1 分组前

3.10.2 分组后

四、数据可视化

4.1 安装matplotlib库

4.2 检测matplotlib库

4.3 箱线图

4.4 散点图

4.5 六边形热力图

4.6 折线图

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