|NO.Z.00041|——————————|BigDataEnd|——|Hadoop&Python.v05|——|Arithmetic.v05|Pandas数据分析库：Pandas数据选取|

一、数据选取：获取数据

### --- 获取数据

import pandas as pd
import numpy as np
df = pd.DataFrame(data = np.random.randint(0,150,size = [150,3]),       # 计算机科⽬的考试成绩
columns=['Python','Tensorflow','Keras'])
df['Python']                                                            # 获取单列，Series
df.Python                                                               # 获取单列，Series
df[['Python','Keras']]                                                  # 获取多列，DataFrame
df[3:15]                                                                # ⾏切⽚

二、标签选择

### --- 标签选择

import pandas as pd
import numpy as np
df = pd.DataFrame(data = np.random.randint(0,150,size = [10,3]),        # 计算机科⽬的考试成绩
                  index = list('ABCDEFGHIJ'),                           # ⾏标签
                  columns=['Python','Tensorflow','Keras'])
df.loc[['A','C','D','F']]                                               # 选取指定⾏标签数据。
df.loc['A':'E',['Python','Keras']]                                      # 根据⾏标签切⽚，选取指定列标签的数据
df.loc[:,['Keras','Tensorflow']]                                        # :默认保留所有⾏
df.loc['E'::2,'Python':'Tensorflow']                                    # ⾏切⽚从标签E开始每2个中取⼀个，列标签进⾏切⽚
df.loc['A','Python']                                                    # 选取标量值

三、位置选择

### --- 位置选择

import pandas as pd
import numpy as np
df = pd.DataFrame(data = np.random.randint(0,150,size = [10,3]),        # 计算机科⽬的考试成绩
                  index = list('ABCDEFGHIJ'),                           # ⾏标签
                  columns=['Python','Tensorflow','Keras'])
df.iloc[4]                                                              # ⽤整数位置选择。
df.iloc[2:8,0:2]                                                        # ⽤整数切⽚，类似NumPy
df.iloc[[1,3,5],[0,2,1]]                                                # 整数列表按位置切⽚
df.iloc[1:3,:]                                                          # ⾏切⽚
df.iloc[:,:2]                                                           # 列切⽚
df.iloc[0,2]                                                            # 选取标量值

四、boolean索引

### --- boolean索引

import pandas as pd
import numpy as np
df = pd.DataFrame(data = np.random.randint(0,150,size = [10,3]),        # 计算机科⽬的考试成绩
                  index = list('ABCDEFGHIJ'),                           # ⾏标签，⽤户
                  columns=['Python','Tensorflow','Keras'])              # 考试科⽬
cond1 = df.Python > 100                                                 # 判断Python分数是否⼤于100，返回值是boolean类型的Series
df[cond1]                                                               # 返回Python分数⼤于100分的⽤户所有考试科⽬数据
cond2 = (df.Python > 50) & (df['Keras'] > 50)                           # &与运算
df[cond2]                                                               # 返回Python和Keras同时⼤于50分的⽤户的所有考试科⽬数据
df[df > 50]                                                             # 选择DataFrame中满⾜条件的值，如果满⾜返回值，不然返回空数据NaN
df[df.index.isin(['A','C','F'])]                                        # isin判断是否在数组中，返回也是boolean类型值

五、赋值操作

### --- 赋值操作

import pandas as pd
import numpy as np
df = pd.DataFrame(data = np.random.randint(0,150,size = [10,3]),        # 计算机科⽬的考试成绩
                  index = list('ABCDEFGHIJ'),                           # ⾏标签，⽤户
                  columns=['Python','Tensorflow','Keras'])              # 考试科⽬
s = pd.Series(data = np.random.randint(0,150,size =
9),index=list('BCDEFGHIJ'),name = 'PyTorch')
df['PyTorch'] = s                                                       # 增加⼀列，DataFrame⾏索引⾃动对⻬
df.loc['A','Python'] = 256                                              # 按标签赋值
df.iloc[3,2] = 512                                                      # 按位置赋值
df.loc[:,'Python'] = np.array([128]*10)                                 # 按NumPy数组进⾏赋值
df[df >= 128] = -df                                                     # 按照where条件进⾏赋值，⼤于等于128变成原来的负数，否则不变
df

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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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