Python数据可视化

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Python数据可视化

2024-01-14 23:48| 来源: 网络整理| 查看: 265

[思维导图] 部分更新：

data.fillna(0,inplace = True) data.drop(range(11),inplace=True) data.drop('I',axis=1,inplace=True) data.dropna(axis=0, how='any', inplace=True) data.dropna(axis=1, how='any', inplace=True)

[ 行删除、列删除、空行删除、空列删除、填充所有空值 ]

Pandas模块中常见函数 pandas.read_csv("path") 读取文件时会自动判定每列的数据类型，如果一列出现多种数据类型使用.info()查看时就会显示当前列属性为object可以使用 "a[字段名].value_counts()" 来对该object类型中各个类型进行统计

data = DataFrame(np.arange(20).reshape(4,5),index = list("ABCD"),columns=list("abcde"))

data.head() 查看前五条记录data.info() 查看各个字段的信息data.describe() 返回对每列数据基本处理后的各个数据（mean/max之类data.shape[0] / len(data) 行数data.shape[1] / data.columns.size 列数data.iloc[1:3,1:3] 切片访问（Index：左闭右开）data.mean[0] + data.mean[1] 参数0表示求行平均值，1表示求列平均值data.columns = ["A","B","C","D"] 修改列名data.index = ["X","Y","Z","W"] 修改行名 DataFrame绘图：

1> Plot折线图

import pandas as pd import numpy as np import matplotlib.pyplot as plt a = pd.DataFrame(np.arange(15).reshape(3,5),columns=['Data-1','Data-2','Data-3','Data-4','Data-5']) # Row_Name:Index b = a.describe() print(b) b.plot() plt.legend(['Data-1','Data-2','Data-3','Data-4','Data-5'],loc="upper left") plt.show()

2> Hist直方图

https://blog.csdn.net/qq_42292831/article/details/89180775https://blog.csdn.net/qq_42292831/article/details/89180775

3> 散点图（ demo涉及DataFrame行列的增加）

import pandas as pd from pandas import DataFrame import numpy as np import matplotlib.pyplot as plt data = DataFrame([{"A":1,"B":2,"C":3}]) #print(data) data = data.append([{"A":11,"B":22,"C":33},{"A":29.558,"B":55,"C":89}]) #print(data) for i in range(20): b = DataFrame([{"A":np.random.rand()*100,"B":np.random.rand()*100,"C":np.random.rand()*100}]) data = data.append(b,ignore_index=True) #print(data) data["D"]=np.random.ranf(23)*100 #print(data) data.plot.scatter(x="B",y="C",color="red",alpha=0.3) plt.show()

向DataFrame格式数据中插入一行与一列：

1> 插入一行

使用append()函数：

1. data = data.append([{"A":1,"B":2,"C":3}, {"A":11,"B":22,"C":33}, {"A":111,"B":222,"C":333}])

2. data = data.append(new_data, ignore_index=True)

2> 插入一列( 行数较少/较多时报错 )

data["New_Name"] = [..., ..., ...]

import pandas as pd from pandas import DataFrame import numpy as np import matplotlib.pyplot as plt data = DataFrame([{"A":1,"B":2,"C":3}]) print(data) data = data.append([{"A":11,"B":22,"C":33},{"A":29.558,"B":55,"C":89}]) print(data) for i in range(20): b = DataFrame([{"A":np.random.rand()*100,"B":np.random.rand()*100,"C":np.random.rand()*100}]) data = data.append(b,ignore_index=True) print(data) data["D"]=np.random.ranf(23)*100 #print(data)

Result：

DataFrame转List：

https://blog.csdn.net/qq_42292831/article/details/89182921

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Python数据可视化

Python数据可视化

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