Python数据分析（3）pandas库的使用：索引，统计函数，排序和NaN数据值处理

曾经终败给现在 2022-05-23 12:35 120阅读 0赞

#coding=gbk
    #pandas 库
    #索引对象的其他功能
    import numpy as np
    import pandas as pd
    #1，更换索引 reindex()
    frame=pd.Series([1,5,6,3],index=[0,3,5,6])
    print(frame)
    # 0    1
    # 3    5
    # 5    6
    # 6    3
    # dtype: int64
    print(frame.reindex(range(6),method='ffill'))   #ffill，填充与前面的值相同
    # 0    1
    # 1    1
    # 2    1
    # 3    5
    # 4    5
    # 5    6
    # dtype: int64
    print(frame.reindex(range(6),method='bfill'))   #bfill，填充与后面的值相同
    # 0    1
    # 1    5
    # 2    5
    # 3    5
    # 4    6
    # 5    6
    # dtype: int64
    #2删除列
    print(frame.drop(3))#删除索引为3 的行
    #还可以删除列，如：print(frame1.drop(['color','object'],axis=1))
    frame2=pd.DataFrame(np.arange(16).reshape(4,4),
                        index=['blue','green','black','yellow'],
                        columns=['color','price','object','real'])
    print(frame2)
    #         color  price  object  real
    # blue        0      1       2     3
    # green       4      5       6     7
    # black       8      9      10    11
    # yellow     12     13      14    15

#统计函数
    print(frame2.sum()) #求每列的总和
    # color     24
    # price     28
    # object    32
    # real      36
    print(frame2.mean())    #求平均值
    print(frame2.describe())    #计算多个统计量
    #            color      price     object       real
    # count   4.000000   4.000000   4.000000   4.000000
    # mean    6.000000   7.000000   8.000000   9.000000
    # std     5.163978   5.163978   5.163978   5.163978    标准方差
    # min     0.000000   1.000000   2.000000   3.000000
    # 25%     3.000000   4.000000   5.000000   6.000000    四分位图，箱形图
    # 50%     6.000000   7.000000   8.000000   9.000000
    # 75%     9.000000  10.000000  11.000000  12.000000
    # max    12.000000  13.000000  14.000000  15.000000

#排序和排位次
    print(frame2.sort_index())  #依据字母的大小正序进行排列
    #         color  price  object  real
    # black       8      9      10    11
    # blue        0      1       2     3
    # green       4      5       6     7
    # yellow     12     13      14    15
    print(frame2.sort_index(ascending=False))   #降序排列
    #         color  price  object  real
    # yellow     12     13      14    15
    # green       4      5       6     7
    # blue        0      1       2     3
    # black       8      9      10    11
    print(frame2.sort_index(axis=1))        #对列进行排序
    #         color  object  price  real
    # blue        0       2      1     3
    # green       4       6      5     7
    # black       8      10      9    11
    # yellow     12      14     13    15
    ser= pd.Series([1,5,2,8,0],index=['blue','green','black','yellow','white'])
    print(ser)
    # blue      1
    # green     5
    # black     2
    # yellow    8
    # white     0
    # dtype: int64
    #对元素进行排序
    # print(ser.order())
    # print(frame2.sort_index(by='color'))
    
    #对排位次进行操作    依据元素的大小进行排序
    print(ser.rank())
    # blue      2.0
    # green     4.0
    # black     3.0
    # yellow    5.0
    # white     1.0
    # dtype: float64
    
    #相关性和协方差
    print(frame2.corr())    #相关性
    #         color  price  object  real
    # color     1.0    1.0     1.0   1.0
    # price     1.0    1.0     1.0   1.0
    # object    1.0    1.0     1.0   1.0
    # real      1.0    1.0     1.0   1.0
    print(frame2.cov()) #协方差
    #             color      price     object       real
    # color   26.666667  26.666667  26.666667  26.666667
    # price   26.666667  26.666667  26.666667  26.666667
    # object  26.666667  26.666667  26.666667  26.666667
    # real    26.666667  26.666667  26.666667  26.666667

#NaN数据值处理
    #1为元素附nan值
    ser1= pd.Series([1,5,np.NaN,np.nan,8],index=['blue','green','black','yellow','white'])
    print(ser1)
    # blue      1.0
    # green     5.0
    # black     NaN
    # yellow    NaN
    # white     8.0
    # dtype: float64
    #2去除nan元素
    print(ser1.dropna())
    # blue     1.0        吧存在nan元素删掉
    # green    5.0
    # white    8.0
    # dtype: float64
    #dataFrame数据结构
    frame3=pd.DataFrame([[1,2,np.nan,np.nan],[2,4,6,np.nan],
                        [8,2,np.nan,np.nan],[np.nan,2,10,np.nan]],
                        index=['blue','green','black','yellow'],
                        columns=['color','price','object','real'])
    print(frame3)
    #         color  price  object  real
    # blue      1.0      2     NaN   NaN
    # green     2.0      4     6.0   NaN
    # black     8.0      2     NaN   NaN
    # yellow    NaN      2    10.0   NaN
    print(frame3.dropna())      #把行或列出现了nan的都删除了，所以为空了
    # Empty DataFrame
    # Columns: [color, price, object, real]
    # Index: []
    print(frame3.dropna(how='all'))#还是为数据 
    #3为nan赋值
    print(frame3.fillna(0)) #为nan值赋值为0
    #         color  price  object  real
    # blue      1.0      2     0.0   0.0
    # green     2.0      4     6.0   0.0
    # black     8.0      2     0.0   0.0
    # yellow    0.0      2    10.0   0.0
    print(frame3.fillna({'real':3}))#为real这一列赋值3
    #         color  price  object  real
    # blue      1.0      2     NaN   3.0
    # green     2.0      4     6.0   3.0
    # black     8.0      2     NaN   3.0
    # yellow    NaN      2    10.0   3.0

\#pandas 中(loc, iloc, 以及 ix 的区别)

#coding=gbk
    #pandas 中(loc, iloc, 以及 ix 的区别)
    # loc：通过行标签索引数据
    # iloc：通过行号索引行数据
    # ix：通过行标签或行号索引数据（基于loc和iloc的混合）
    import pandas as pd
    data = pd.DataFrame([[1,2,3],[4,5,6]],
                        index = ['a','b'],
                        columns=['c','d','e'])
    print(data)
    #   c  d  e
    # a  1  2  3
    # b  4  5  6
    print(data.loc['a'])    #输入行的标签 
    # c    1
    # d    2
    # e    3
    # Name: a, dtype: int64
    # print(data.iloc['a'])  输出出错，需要使用行号，是数值型
    print(data.iloc[1])
    # c    4
    # d    5
    # e    6
    # Name: b, dtype: int64
    print(data['c'])        #输出打印出列的数据
    # a    1
    # b    4
    # Name: c, dtype: int64
    print('使用ix 方法')
    print(data.ix['a'])     #与下方输出的数据一样的
    # c    1
    # d    2
    # e    3
    # Name: a, dtype: int64
    print(data.ix[0])
    # c    1
    # d    2
    # e    3
    # Name: a, dtype: int64

print('-----')

print('-----')
    #1.修改列名
    data = pd.DataFrame(np.arange(12).reshape(6,2),columns =['a','b'])
    print(data)
    #     a   b
    # 0   0   1
    # 1   2   3
    # 2   4   5
    # 3   6   7
    # 4   8   9
    # 5  10  11
    data.rename(columns ={'a':'key1','b':'key2'},inplace = True)    #inplace 是在原数据上修改
    print(data)
    #     a   b
    # 0   0   1
    # 1   2   3
    # 2   4   5
    # 3   6   7
    # 4   8   9
    # 5  10  11
    d = pd.Series([1,2,3,4,2,4,1])
    print(d)
    print(d.unique())
    print(d.value_counts())
    i= d.isin([1,2,5,7])
    print(i)    ##计算一个“Series各值是否包含传入的值序列中”的布尔数组
    # 0     True
    # 1     True
    # 2    False
    # 3    False
    # 4     True
    # 5    False
    # 6     True
    # dtype: bool
    
    #2,值替换 replace（）
    data = pd.DataFrame(np.arange(6).reshape(3,2),columns =['a','b'])
    print(data)
    #    a  b
    # 0  0  1
    # 1  2  3
    # 2  4  5
    data['a'] = data['a'].replace({2:100,4:200}) #将2和 4 替换成100 和 200
    print(data)
    #      a  b
    # 0    0  1
    # 1  100  3
    # 2  200  5
    #修改值
    cloumns = ['age','name','height','sex']
    data = pd.DataFrame([[12,'zhangsan',170,'male'],[18,'lisi',178,'female']],columns=cloumns)
    print(data)
    #    age      name  height     sex
    # 0   12  zhangsan     170    male
    # 1   18      lisi     178  female
    #修改张三的身高为190
    data['height'] = data['height'].replace({170:190})
    print(data)
    #3数据选取
    print(data[data['b']>4])        #选取b行中大于4的数据
    #      a  b
    # 2  200  5
    
    #创建新的变量test
    data = pd.DataFrame(np.arange(12).reshape(3,4),columns = ['a','b','c','d'])
    print(data)
    #    a  b   c   d
    # 0  0  1   2   3
    # 1  4  5   6   7
    # 2  8  9  10  11
    data['test'] = None
    s = data['b']> 4
    data['test'] = np.where(s,1,0)
    print(data)
    #    a  b   c   d  test
    # 0  0  1   2   3     0
    # 1  4  5   6   7     1
    # 2  8  9  10  11     1

import pandas as pd
    import numpy as np
    data = pd.DataFrame(np.arange(12).reshape(3,4),columns = ['a','b','c','d'])
    print(data)
    #    a  b   c   d
    # 0  0  1   2   3
    # 1  4  5   6   7
    # 2  8  9  10  11
    print(data['a'][1]) #返回第 'a'列的第一个元素    4
    print(data.ix[0,1]) #返回第一行第二列的元素    1
    print(data.iloc[0:2,0:2])       #输出指定的行列数的数据
    #    a  b
    # 0  0  1
    # 1  4  5

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[1240]: /images/20220523/da814343c2fb43d5b627868e0442727a.png