90个Pandas案例

• 如何使用列表和字典创建 Series
  • 使用列表创建 Series
  • 使用 name 参数创建 Series
  • 使用简写的列表创建 Series
  • 使用字典创建 Series
• 如何使用 Numpy 函数创建 Series
• 如何获取 Series 的索引和值
• 如何在创建 Series 时指定索引
• 如何获取 Series 的大小和形状
• 如何获取 Series 开始或末尾几行数据
  • Head()
  • Tail()
  • Take()
• 使用切片获取 Series 子集
• 如何创建 DataFrame
• 如何设置 DataFrame 的索引和列信息
• 如何重命名 DataFrame 的列名称
• 如何根据 Pandas 列中的值从 DataFrame 中选择或过滤行
• 在 DataFrame 中使用“isin”过滤多行
• 迭代 DataFrame 的行和列
• 如何通过名称或索引删除 DataFrame 的列
• 向 DataFrame 中新增列
• 如何从 DataFrame 中获取列标题列表
• 如何随机生成 DataFrame
• 如何选择 DataFrame 的多个列
• 如何将字典转换为 DataFrame
• 使用 ioc 进行切片
• 检查 DataFrame 中是否是空的
• 在创建 DataFrame 时指定索引和列名称
• 使用 iloc 进行切片
• iloc 和 loc 的区别
• 使用时间索引创建空 DataFrame
• 如何改变 DataFrame 列的排序
• 检查 DataFrame 列的数据类型
• 更改 DataFrame 指定列的数据类型
• 如何将列的数据类型转换为 DateTime 类型
• 将 DataFrame 列从 floats 转为 ints
• 如何把 dates 列转换为 DateTime 类型
• 两个 DataFrame 相加
• 在 DataFrame 末尾添加额外的行
• 为指定索引添加新行
• 如何使用 for 循环添加行
• 在 DataFrame 顶部添加一行
• 如何向 DataFrame 中动态添加行
• 在任意位置插入行
• 使用时间戳索引向 DataFrame 中添加行
• 为不同的行填充缺失值
• append, concat 和 combine_first 示例
• 获取行和列的平均值
• 计算行和列的总和
• 连接两列
• 过滤包含某字符串的行
• 过滤索引中包含某字符串的行
• 使用 AND 运算符过滤包含特定字符串值的行
• 查找包含某字符串的所有行
• 如果行中的值包含字符串，则创建与字符串相等的另一列
• 计算 pandas group 中每组的行数
• 检查字符串是否在 DataFrme 中
• 从 DataFrame 列中获取唯一行值
• 计算 DataFrame 列的不同值
• 删除具有重复索引的行
• 删除某些列具有重复值的行
• 从 DataFrame 单元格中获取值
• 使用 DataFrame 中的条件索引获取单元格上的标量值
• 设置 DataFrame 的特定单元格值
• 从 DataFrame 行获取单元格值
• 用字典替换 DataFrame 列中的值
• 统计基于某一列的一列的数值
• 处理 DataFrame 中的缺失值
• 删除包含任何缺失数据的行
• 删除 DataFrame 中缺失数据的列
• 按降序对索引值进行排序
• 按降序对列进行排序
• 使用 rank 方法查找 DataFrame 中元素的排名
• 在多列上设置索引
• 确定 DataFrame 的周期索引和列
• 导入 CSV 指定特定索引
• 将 DataFrame 写入 csv
• 使用 Pandas 读取 csv 文件的特定列
• Pandas 获取 CSV 列的列表
• 找到列值最大的行
• 使用查询方法进行复杂条件选择
• 检查 Pandas 中是否存在列
• 为特定列从 DataFrame 中查找 n-smallest 和 n-largest 值
• 从 DataFrame 中查找所有列的最小值和最大值
• 在 DataFrame 中找到最小值和最大值所在的索引位置
• 计算 DataFrame Columns 的累积乘积和累积总和
• 汇总统计
• 查找 DataFrame 的均值、中值和众数
• 测量 DataFrame 列的方差和标准偏差
• 计算 DataFrame 列之间的协方差
• 计算 Pandas 中两个 DataFrame 对象之间的相关性
• 计算 DataFrame 列的每个单元格的百分比变化
• 在 Pandas 中向前和向后填充 DataFrame 列的缺失值
• 在 Pandas 中使用非分层索引使用 Stacking
• 使用分层索引对 Pandas 进行拆分
• Pandas 获取 HTML 页面上 table 数据

  如何使用列表和字典创建 Series

  使用列表创建 Series

  ```python import pandas as pd

ser1 = pd.Series([1.5, 2.5, 3, 4.5, 5.0, 6]) print(ser1)

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#### OutPut
```python
0    1.5
1    2.5
2    3.0
3    4.5
4    5.0
5    6.0
dtype: float64

使用 name 参数创建 Series

import pandas as pd
ser2 = pd.Series(["India", "Canada", "Germany"], name="Countries")
print(ser2)

OutPut

0      India
1     Canada
2    Germany
Name: Countries, dtype: object

使用简写的列表创建 Series

import pandas as pd
ser3 = pd.Series(["A"]*4)
print(ser3)

OutPut

0    A
1    A
2    A
3    A
dtype: object

使用字典创建 Series

import pandas as pd
ser4 = pd.Series({"India": "New Delhi",
                  "Japan": "Tokyo",
                  "UK": "London"})
print(ser4)

OutPut

India    New Delhi
Japan        Tokyo
UK          London
dtype: object

如何使用 Numpy 函数创建 Series

import pandas as pd
import numpy as np
ser1 = pd.Series(np.linspace(1, 10, 5))
print(ser1)
ser2 = pd.Series(np.random.normal(size=5))
print(ser2)

OutPut

0     1.00
1     3.25
2     5.50
3     7.75
4    10.00
dtype: float64
0   -1.694452
1   -1.570006
2    1.713794
3    0.338292
4    0.803511
dtype: float64

如何获取 Series 的索引和值

import pandas as pd
import numpy as np
ser1 = pd.Series({"India": "New Delhi",
                  "Japan": "Tokyo",
                  "UK": "London"})
print(ser1.values)
print(ser1.index)
print("\n")
ser2 = pd.Series(np.random.normal(size=5))
print(ser2.index)
print(ser2.values)

OutPut

['New Delhi' 'Tokyo' 'London']
Index(['India', 'Japan', 'UK'], dtype='object')
RangeIndex(start=0, stop=5, step=1)
[ 0.66265478 -0.72222211  0.3608642   1.40955436  1.3096732 ]

如何在创建 Series 时指定索引

import pandas as pd
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print(ser1)

OutPut

IND        India
CAN       Canada
AUS    Australia
JAP        Japan
GER      Germany
FRA       France
dtype: object

如何获取 Series 的大小和形状

import pandas as pd
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print(len(ser1))
print(ser1.shape)
print(ser1.size)

OutPut

6
(6,)
6

如何获取 Series 开始或末尾几行数据

Head()

import pandas as pd
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print("-----Head()-----")
print(ser1.head())
print("\n\n-----Head(2)-----")
print(ser1.head(2))

OutPut

-----Head()-----
IND        India
CAN       Canada
AUS    Australia
JAP        Japan
GER      Germany
dtype: object
-----Head(2)-----
IND     India
CAN    Canada
dtype: object

Tail()

import pandas as pd
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print("-----Tail()-----")
print(ser1.tail())
print("\n\n-----Tail(2)-----")
print(ser1.tail(2))

OutPut

-----Tail()-----
CAN       Canada
AUS    Australia
JAP        Japan
GER      Germany
FRA       France
dtype: object
-----Tail(2)-----
GER    Germany
FRA     France
dtype: object

Take()

import pandas as pd
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print("-----Take()-----")
print(ser1.take([2, 4, 5]))

OutPut

-----Take()-----
AUS    Australia
GER      Germany
FRA       France
dtype: object

使用切片获取 Series 子集

import pandas as pd
num = [000, 100, 200, 300, 400, 500, 600, 700, 800, 900]
idx = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
series = pd.Series(num, index=idx)
print("\n [2:2] \n")
print(series[2:4])
print("\n [1:6:2] \n")
print(series[1:6:2])
print("\n [:6] \n")
print(series[:6])
print("\n [4:] \n")
print(series[4:])
print("\n [:4:2] \n")
print(series[:4:2])
print("\n [4::2] \n")
print(series[4::2])
print("\n [::-1] \n")
print(series[::-1])

OutPut

 [2:2]
C    200
D    300
dtype: int64
 [1:6:2]
B    100
D    300
F    500
dtype: int64
 [:6]
A      0
B    100
C    200
D    300
E    400
F    500
dtype: int64
 [4:]
E    400
F    500
G    600
H    700
I    800
J    900
dtype: int64
 [:4:2]
A      0
C    200
dtype: int64
 [4::2]
E    400
G    600
I    800
dtype: int64
 [::-1]
J    900
I    800
H    700
G    600
F    500
E    400
D    300
C    200
B    100
A      0
dtype: int64

如何创建 DataFrame

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp00'],
    'Name': ['John Doe', 'William Spark'],
    'Occupation': ['Chemist', 'Statistician'],
    'Date Of Join': ['2018-01-25', '2018-01-26'],
    'Age': [23, 24]})
print(employees)

OutPut

   Age Date Of Join EmpCode           Name    Occupation
0   23   2018-01-25  Emp001       John Doe       Chemist
1   24   2018-01-26   Emp00  William Spark  Statistician

如何设置 DataFrame 的索引和列信息

import pandas as pd
employees = pd.DataFrame(
    data={'Name': ['John Doe', 'William Spark'],
          'Occupation': ['Chemist', 'Statistician'],
          'Date Of Join': ['2018-01-25', '2018-01-26'],
          'Age': [23, 24]},
    index=['Emp001', 'Emp002'],
    columns=['Name', 'Occupation', 'Date Of Join', 'Age'])
print(employees)

OutPut

                 Name    Occupation Date Of Join  Age
Emp001       John Doe       Chemist   2018-01-25   23
Emp002  William Spark  Statistician   2018-01-26   24

如何重命名 DataFrame 的列名称

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp00'],
    'Name': ['John Doe', 'William Spark'],
    'Occupation': ['Chemist', 'Statistician'],
    'Date Of Join': ['2018-01-25', '2018-01-26'],
    'Age': [23, 24]})
employees.columns = ['EmpCode', 'EmpName', 'EmpOccupation', 'EmpDOJ', 'EmpAge']
print(employees)

OutPut

   EmpCode     EmpName EmpOccupation         EmpDOJ        EmpAge
0       23  2018-01-25        Emp001       John Doe       Chemist
1       24  2018-01-26         Emp00  William Spark  Statistician

如何根据 Pandas 列中的值从 DataFrame 中选择或过滤行

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})
print("\nUse == operator\n")
print(employees.loc[employees['Age'] == 23])
print("\nUse < operator\n")
print(employees.loc[employees['Age'] < 30])
print("\nUse != operator\n")
print(employees.loc[employees['Occupation'] != 'Statistician'])
print("\nMultiple Conditions\n")
print(employees.loc[(employees['Occupation'] != 'Statistician') &
                    (employees['Name'] == 'John')])

OutPut

Use == operator
   Age Date Of Join EmpCode  Name Occupation
0   23   2018-01-25  Emp001  John    Chemist
Use < operator
   Age Date Of Join EmpCode   Name    Occupation
0   23   2018-01-25  Emp001   John       Chemist
1   24   2018-01-26  Emp002    Doe  Statistician
3   29   2018-02-26  Emp004  Spark  Statistician
Use != operator
   Age Date Of Join EmpCode  Name  Occupation
0   23   2018-01-25  Emp001  John     Chemist
4   40   2018-03-16  Emp005  Mark  Programmer
Multiple Conditions
   Age Date Of Join EmpCode  Name Occupation
0   23   2018-01-25  Emp001  John    Chemist

在 DataFrame 中使用“isin”过滤多行

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})
print("\nUse isin operator\n")
print(employees.loc[employees['Occupation'].isin(['Chemist','Programmer'])])
print("\nMultiple Conditions\n")
print(employees.loc[(employees['Occupation'] == 'Chemist') |
                    (employees['Name'] == 'John') &
                    (employees['Age'] < 30)])

OutPut

Use isin operator
   Age Date Of Join EmpCode  Name  Occupation
0   23   2018-01-25  Emp001  John     Chemist
4   40   2018-03-16  Emp005  Mark  Programmer
Multiple Conditions
   Age Date Of Join EmpCode  Name Occupation
0   23   2018-01-25  Emp001  John    Chemist

迭代 DataFrame 的行和列

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})
print("\n Example iterrows \n")
for index, col in employees.iterrows():
    print(col['Name'], "--", col['Age'])
print("\n Example itertuples \n")
for row in employees.itertuples(index=True, name='Pandas'):
    print(getattr(row, "Name"), "--", getattr(row, "Age"))

OutPut

 Example iterrows
John -- 23
Doe -- 24
William -- 34
Spark -- 29
Mark -- 40
 Example itertuples
John -- 23
Doe -- 24
William -- 34
Spark -- 29
Mark -- 40

如何通过名称或索引删除 DataFrame 的列

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})
print(employees)
print("\n Drop Column by Name \n")
employees.drop('Age', axis=1, inplace=True)
print(employees)
print("\n Drop Column by Index \n")
employees.drop(employees.columns[[0,1]], axis=1, inplace=True)
print(employees)

OutPut

   Age Date Of Join EmpCode     Name    Occupation
0   23   2018-01-25  Emp001     John       Chemist
1   24   2018-01-26  Emp002      Doe  Statistician
2   34   2018-01-26  Emp003  William  Statistician
3   29   2018-02-26  Emp004    Spark  Statistician
4   40   2018-03-16  Emp005     Mark    Programmer
 Drop Column by Name
  Date Of Join EmpCode     Name    Occupation
0   2018-01-25  Emp001     John       Chemist
1   2018-01-26  Emp002      Doe  Statistician
2   2018-01-26  Emp003  William  Statistician
3   2018-02-26  Emp004    Spark  Statistician
4   2018-03-16  Emp005     Mark    Programmer
 Drop Column by Index
      Name    Occupation
0     John       Chemist
1      Doe  Statistician
2  William  Statistician
3    Spark  Statistician
4     Mark    Programmer

向 DataFrame 中新增列

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})
employees['City'] = ['London', 'Tokyo', 'Sydney', 'London', 'Toronto']
print(employees)

OutPut

   Age Date Of Join EmpCode     Name    Occupation     City
0   23   2018-01-25  Emp001     John       Chemist   London
1   24   2018-01-26  Emp002      Doe  Statistician    Tokyo
2   34   2018-01-26  Emp003  William  Statistician   Sydney
3   29   2018-02-26  Emp004    Spark  Statistician   London
4   40   2018-03-16  Emp005     Mark    Programmer  Toronto

如何从 DataFrame 中获取列标题列表

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})
print(list(employees))
print(list(employees.columns.values))
print(employees.columns.tolist())

OutPut

['Age', 'Date Of Join', 'EmpCode', 'Name', 'Occupation']
['Age', 'Date Of Join', 'EmpCode', 'Name', 'Occupation']
['Age', 'Date Of Join', 'EmpCode', 'Name', 'Occupation']

如何随机生成 DataFrame

import pandas as pd
import numpy as np
np.random.seed(5)
df_random = pd.DataFrame(np.random.randint(100, size=(10, 6)),
                         columns=list('ABCDEF'),
                         index=['Row-{}'.format(i) for i in range(10)])
print(df_random)s

OutPut

        A   B   C   D   E   F
Row-0  99  78  61  16  73   8
Row-1  62  27  30  80   7  76
Row-2  15  53  80  27  44  77
Row-3  75  65  47  30  84  86
Row-4  18   9  41  62   1  82
Row-5  16  78   5  58   0  80
Row-6   4  36  51  27  31   2
Row-7  68  38  83  19  18   7
Row-8  30  62  11  67  65  55
Row-9   3  91  78  27  29  33

如何选择 DataFrame 的多个列

import pandas as pd
employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})
df = employees[['EmpCode', 'Age', 'Name']]
print(df)

OutPut

  EmpCode  Age     Name
0  Emp001   23     John
1  Emp002   24      Doe
2  Emp003   34  William
3  Emp004   29    Spark
4  Emp005   40     Mark

如何将字典转换为 DataFrame

import pandas as pd
data = ({'Age': [30, 20, 22, 40, 32, 28, 39],
                   'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
                             'Red'],
                   'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
                            'Melon', 'Beans'],
                   'Height': [165, 70, 120, 80, 180, 172, 150],
                   'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
                   'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
                   })
print(data)
df = pd.DataFrame(data)
print(df)

OutPut

{'Height': [165, 70, 120, 80, 180, 172, 150], 'Food': ['Steak', 'Lamb', 'Mango',
 'Apple', 'Cheese', 'Melon', 'Beans'], 'Age': [30, 20, 22, 40, 32, 28, 39], 'Sco
re': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2], 'Color': ['Blue', 'Green', 'Red', 'Whi
te', 'Gray', 'Black', 'Red'], 'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX'
]}
   Age  Color    Food  Height  Score State
0   30   Blue   Steak     165    4.6    NY
1   20  Green    Lamb      70    8.3    TX
2   22    Red   Mango     120    9.0    FL
3   40  White   Apple      80    3.3    AL
4   32   Gray  Cheese     180    1.8    AK
5   28  Black   Melon     172    9.5    TX
6   39    Red   Beans     150    2.2    TX

使用 ioc 进行切片

import pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
                   'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
                             'Red'],
                   'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
                            'Melon', 'Beans'],
                   'Height': [165, 70, 120, 80, 180, 172, 150],
                   'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
                   'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
                   },
                  index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
                         'Christina', 'Cornelia'])
print("\n -- Selecting a single row with .loc with a string -- \n")
print(df.loc['Penelope'])
print("\n -- Selecting multiple rows with .loc with a list of strings -- \n")
print(df.loc[['Cornelia', 'Jane', 'Dean']])
print("\n -- Selecting multiple rows with .loc with slice notation -- \n")
print(df.loc['Aaron':'Dean'])

OutPut

 -- Selecting a single row with .loc with a string --
Age          40
Color     White
Food      Apple
Height       80
Score       3.3
State        AL
Name: Penelope, dtype: object
 -- Selecting multiple rows with .loc with a list of strings --
          Age Color    Food  Height  Score State
Cornelia   39   Red   Beans     150    2.2    TX
Jane       30  Blue   Steak     165    4.6    NY
Dean       32  Gray  Cheese     180    1.8    AK
 -- Selecting multiple rows with .loc with slice notation --
          Age  Color    Food  Height  Score State
Aaron      22    Red   Mango     120    9.0    FL
Penelope   40  White   Apple      80    3.3    AL
Dean       32   Gray  Cheese     180    1.8    AK

检查 DataFrame 中是否是空的

import pandas as pd
df = pd.DataFrame()
if df.empty:
    print('DataFrame is empty!')

OutPut

DataFrame is empty!

在创建 DataFrame 时指定索引和列名称

import pandas as pd
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
df = pd.DataFrame(values, index=code, columns=['Country'])
print(df)

OutPut

       Country
IND      India
CAN     Canada
AUS  Australia
JAP      Japan
GER    Germany
FRA     France

使用 iloc 进行切片

import pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
                   'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
                             'Red'],
                   'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
                            'Melon', 'Beans'],
                   'Height': [165, 70, 120, 80, 180, 172, 150],
                   'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
                   'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
                   },
                  index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
                         'Christina', 'Cornelia'])
print("\n -- Selecting a single row with .iloc with an integer -- \n")
print(df.iloc[4])
print("\n -- Selecting multiple rows with .iloc with a list of integers -- \n")
print(df.iloc[[2, -2]])
print("\n -- Selecting multiple rows with .iloc with slice notation -- \n")
print(df.iloc[:5:3])

OutPut

 -- Selecting a single row with .iloc with an integer --
Age           32
Color       Gray
Food      Cheese
Height       180
Score        1.8
State         AK
Name: Dean, dtype: object
 -- Selecting multiple rows with .iloc with a list of integers --
           Age  Color   Food  Height  Score State
Aaron       22    Red  Mango     120    9.0    FL
Christina   28  Black  Melon     172    9.5    TX
 -- Selecting multiple rows with .iloc with slice notation --
          Age  Color   Food  Height  Score State
Jane       30   Blue  Steak     165    4.6    NY
Penelope   40  White  Apple      80    3.3    AL

iloc 和 loc 的区别

• loc 索引器还可以进行布尔选择，例如，如果我们想查找 Age 小于 30 的所有行并仅返回 Color 和 Height 列，我们可以执行以下操作。我们可以用 iloc 复制它，但我们不能将它传递给一个布尔系列，必须将布尔系列转换为 numpy 数组
• loc 从索引中获取具有特定标签的行（或列）
• iloc 在索引中的特定位置获取行（或列）（因此它只需要整数） ```python import pandas as pd

df = pd.DataFrame({‘Age’: [30, 20, 22, 40, 32, 28, 39], ‘Color’: [‘Blue’, ‘Green’, ‘Red’, ‘White’, ‘Gray’, ‘Black’, ‘Red’], ‘Food’: [‘Steak’, ‘Lamb’, ‘Mango’, ‘Apple’, ‘Cheese’, ‘Melon’, ‘Beans’], ‘Height’: [165, 70, 120, 80, 180, 172, 150], ‘Score’: [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2], ‘State’: [‘NY’, ‘TX’, ‘FL’, ‘AL’, ‘AK’, ‘TX’, ‘TX’] }, index=[‘Jane’, ‘Nick’, ‘Aaron’, ‘Penelope’, ‘Dean’, ‘Christina’, ‘Cornelia’])

print(“\n — loc — \n”) print(df.loc[df[‘Age’] < 30, [‘Color’, ‘Height’]])

print(“\n — iloc — \n”) print(df.iloc[(df[‘Age’] < 30).values, [1, 3]])

<a name="Awmnw"></a>
### OutPut
```python
 -- loc --
           Color  Height
Nick       Green      70
Aaron        Red     120
Christina  Black     172
 -- iloc --
           Color  Height
Nick       Green      70
Aaron        Red     120
Christina  Black     172

使用时间索引创建空 DataFrame

import datetime
import pandas as pd
todays_date = datetime.datetime.now().date()
index = pd.date_range(todays_date, periods=10, freq='D')
columns = ['A', 'B', 'C']
df = pd.DataFrame(index=index, columns=columns)
df = df.fillna(0)
print(df)

OutPut

            A  B  C
2018-09-30  0  0  0
2018-10-01  0  0  0
2018-10-02  0  0  0
2018-10-03  0  0  0
2018-10-04  0  0  0
2018-10-05  0  0  0
2018-10-06  0  0  0
2018-10-07  0  0  0
2018-10-08  0  0  0
2018-10-09  0  0  0

如何改变 DataFrame 列的排序

import pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
                   'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
                             'Red'],
                   'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
                            'Melon', 'Beans'],
                   'Height': [165, 70, 120, 80, 180, 172, 150],
                   'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
                   'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
                   },
                  index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
                         'Christina', 'Cornelia'])
print("\n -- Change order using columns -- \n")
new_order = [3, 2, 1, 4, 5, 0]
df = df[df.columns[new_order]]
print(df)
print("\n -- Change order using reindex -- \n")
df = df.reindex(['State', 'Color', 'Age', 'Food', 'Score', 'Height'], axis=1)
print(df)

OutPut

 -- Change order using columns --
           Height    Food  Color  Score State  Age
Jane          165   Steak   Blue    4.6    NY   30
Nick           70    Lamb  Green    8.3    TX   20
Aaron         120   Mango    Red    9.0    FL   22
Penelope       80   Apple  White    3.3    AL   40
Dean          180  Cheese   Gray    1.8    AK   32
Christina     172   Melon  Black    9.5    TX   28
Cornelia      150   Beans    Red    2.2    TX   39
 -- Change order using reindex --
          State  Color  Age    Food  Score  Height
Jane         NY   Blue   30   Steak    4.6     165
Nick         TX  Green   20    Lamb    8.3      70
Aaron        FL    Red   22   Mango    9.0     120
Penelope     AL  White   40   Apple    3.3      80
Dean         AK   Gray   32  Cheese    1.8     180
Christina    TX  Black   28   Melon    9.5     172
Cornelia     TX    Red   39   Beans    2.2     150

检查 DataFrame 列的数据类型

import pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
                   'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
                             'Red'],
                   'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
                            'Melon', 'Beans'],
                   'Height': [165, 70, 120, 80, 180, 172, 150],
                   'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
                   'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
                   },
                  index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
                         'Christina', 'Cornelia'])
print(df.dtypes)