|NO.Z.00044|——————————|BigDataEnd|——|Hadoop&Python.v08|——|Arithmetic.v08|Pandas数据分析库：Pandas数据转换|

一、数据转换：轴和元素替换

### --- 轴和元素替换

import numpy as np
import pandas as pd
df = pd.DataFrame(data = np.random.randint(0,10,size = (10,3)),
                  index = list('ABCDEFHIJK'),
                  columns=['Python','Tensorflow','Keras'])
df.iloc[4,2] = None                                                 # 空数据
#1、重命名轴索引
df.rename(index = {'A':'AA','B':'BB'},columns = {'Python':'⼈⼯智能'})
#2、替换值
df.replace(3,1024)                                                   # 将3替换为1024
df.replace([0,7],2048)                                               # 将0和7替换为2048
df.replace({0:512,np.nan:998})                                       # 根据字典键值对进⾏替换
df.replace({'Python':2},-1024)                                       # 将Python这⼀列中等于2的，替换为-1024

二、map Series元素改变

### --- map Series元素改变

import numpy as np
import pandas as pd
df = pd.DataFrame(data = np.random.randint(0,10,size = (10,3)),
                  index = list('ABCDEFHIJK'),
                  columns=['Python','Tensorflow','Keras'])
df.iloc[4,2] = None                                                     # 空数据
# 1、map批量元素改变，Series专有
df['Keras'].map({1:'Hello',5:'World',7:'AI'})                           # 字典映射
df['Python'].map(lambda x:True if x >=5 else False)                     # 隐式函数映射
def convert(x):                                                         # 显示函数映射
    if x%3 == 0:
        return True
    elif x%3 == 1:
        return False
df['Tensorflow'].map(convert)

三、apply元素改变。既⽀持 Series，也⽀持 DataFrame

### --- apply元素改变。既⽀持 Series，也⽀持 DataFrame

import numpy as np
import pandas as pd
df = pd.DataFrame(data = np.random.randint(0,10,size = (10,3)),
                  index = list('ABCDEFHIJK'),
                  columns=['Python','Tensorflow','Keras'])
df.iloc[4,2] = None                                                     # 空数据
# 1、apply 应⽤⽅法数据转换，通⽤
# Series，其中x是Series中元素
df['Keras'].apply(lambda x:True if x >5 else False)
# DataFrame，其中的x是DataFrame中列或者⾏，是Series
df.apply(lambda x : x.median(),axis = 0)                                # 列的中位数
def convert(x):                                                         # ⾃定义⽅法
    return (x.mean().round(1),x.count())
df.apply(convert,axis = 1)                                              # ⾏平均值，计数
# 2、applymap DataFrame专有
df.applymap(lambda x : x + 100)                                         # 计算DataFrame中每个元素

四、transform变形⾦刚

### --- transform变形⾦刚

import numpy as np
import pandas as pd
df = pd.DataFrame(data = np.random.randint(0,10,size = (10,3)),
                  index = list('ABCDEFHIJK'),
                  columns=['Python','Tensorflow','Keras'])
df.iloc[4,2] = None                                                     # 空数据
# 1、⼀列执⾏多项计算
df['Python'].transform([np.sqrt,np.exp])                                # Series处理
def convert(x):
    if x.mean() > 5:
        x *= 10
    else:
        x *= -10
    return x
# 2、多列执⾏不同计算
df.transform({'Python':convert,'Tensorflow':np.max,'Keras':np.min})     # DataFrame处理

五、重排随机抽样哑变量

### --- 重排随机抽样哑变量

import numpy as np
import pandas as pd
df = pd.DataFrame(data = np.random.randint(0,10,size = (10,3)),
                  index = list('ABCDEFHIJK'),
                  columns=['Python','Tensorflow','Keras'])

ran = np.random.permutation(10)                                         # 随机重排
df.take(ran)                                                            # 重排DataFrame
df.take(np.random.randint(0,10,size = 15))                              # 随机抽样

# 哑变量，独热编码，1表示有，0表示没有
df = pd.DataFrame({'key':['b','b','a','c','a','b']})
pd.get_dummies(df,prefix='',prefix_sep='')

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Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart

                                                                                                                                                   ——W.S.Landor

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