Python数据可视化 - Pandas + DataFrame( 作图 )

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

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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

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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()

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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：

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DataFrame转List：

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