整理了90个Pandas案例，强烈建议收藏！
俊红的数据分析之路
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2022-02-11 23:43
给大家分享90个Pandas案例。
文章很长，高低要忍一下，如果忍不了，那就收藏吧，总会用到的
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
1如何使用列表和字典创建 Series
使用列表创建 Series
import pandas as pd
ser1 = pd.Series([1.5, 2.5, 3, 4.5, 5.0, 6])
print(ser1)
Output:
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
2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
3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 ]
4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
5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
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
7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
8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
9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
10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
11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
12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
13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
14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
15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
16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']
17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)
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
18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
19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
20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
21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!
22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
23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
24iloc 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）
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]])
Output:
-- loc --
Color Height
Nick Green 70
Aaron Red 120
Christina Black 172
-- iloc --
Color Height
Nick Green 70
Aaron Red 120
Christina Black 172
25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
26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
27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)
Output:
Age int64
Color object
Food object
Height int64
Score float64
State object
dtype: object
28更改 DataFrame 指定列的数据类型
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)
df['Age'] = df['Age'].astype(str)
print(df.dtypes)
Output:
Age int64
Color object
Food object
Height int64
Score float64
State object
dtype: object
Age object
Color object
Food object
Height int64
Score float64
State object
dtype: object
29如何将列的数据类型转换为 DateTime 类型
import pandas as pd
df = pd.DataFrame({'DateOFBirth': [1349720105, 1349806505, 1349892905,
1349979305, 1350065705, 1349792905,
1349730105],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n----------------Before---------------\n")
print(df.dtypes)
print(df)
df['DateOFBirth'] = pd.to_datetime(df['DateOFBirth'], unit='s')
print("\n----------------After----------------\n")
print(df.dtypes)
print(df)
Output:
----------------Before---------------
DateOFBirth int64
State object
dtype: object
DateOFBirth State
Jane 1349720105 NY
Nick 1349806505 TX
Aaron 1349892905 FL
Penelope 1349979305 AL
Dean 1350065705 AK
Christina 1349792905 TX
Cornelia 1349730105 TX
----------------After----------------
DateOFBirth datetime64[ns]
State object
dtype: object
DateOFBirth State
Jane 2012-10-08 18:15:05 NY
Nick 2012-10-09 18:15:05 TX
Aaron 2012-10-10 18:15:05 FL
Penelope 2012-10-11 18:15:05 AL
Dean 2012-10-12 18:15:05 AK
Christina 2012-10-09 14:28:25 TX
Cornelia 2012-10-08 21:01:45 TX
30将 DataFrame 列从 floats 转为 ints
import pandas as pd
df = pd.DataFrame({'DailyExp': [75.7, 56.69, 55.69, 96.5, 84.9, 110.5,
58.9],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n----------------Before---------------\n")
print(df.dtypes)
print(df)
df['DailyExp'] = df['DailyExp'].astype(int)
print("\n----------------After----------------\n")
print(df.dtypes)
print(df)
Output:
----------------Before---------------
DailyExp float64
State object
dtype: object
DailyExp State
Jane 75.70 NY
Nick 56.69 TX
Aaron 55.69 FL
Penelope 96.50 AL
Dean 84.90 AK
Christina 110.50 TX
Cornelia 58.90 TX
----------------After----------------
DailyExp int32
State object
dtype: object
DailyExp State
Jane 75 NY
Nick 56 TX
Aaron 55 FL
Penelope 96 AL
Dean 84 AK
Christina 110 TX
Cornelia 58 TX
31如何把 dates 列转换为 DateTime 类型
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n----------------Before---------------\n")
print(df.dtypes)
df['DateOfBirth'] = df['DateOfBirth'].astype('datetime64')
print("\n----------------After----------------\n")
print(df.dtypes)
Output:
----------------Before---------------
DateOfBirth object
State object
dtype: object
----------------After----------------
DateOfBirth datetime64[ns]
State object
dtype: object
32两个 DataFrame 相加
import pandas as pd
df1 = pd.DataFrame({'Age': [30, 20, 22, 40], 'Height': [165, 70, 120, 80],
'Score': [4.6, 8.3, 9.0, 3.3], 'State': ['NY', 'TX',
'FL', 'AL']},
index=['Jane', 'Nick', 'Aaron', 'Penelope'])
df2 = pd.DataFrame({'Age': [32, 28, 39], 'Color': ['Gray', 'Black', 'Red'],
'Food': ['Cheese', 'Melon', 'Beans'],
'Score': [1.8, 9.5, 2.2], 'State': ['AK', 'TX', 'TX']},
index=['Dean', 'Christina', 'Cornelia'])
df3 = df1.append(df2, sort=True)
print(df3)
Output:
Age Color Food Height Score State
Jane 30 NaN NaN 165.0 4.6 NY
Nick 20 NaN NaN 70.0 8.3 TX
Aaron 22 NaN NaN 120.0 9.0 FL
Penelope 40 NaN NaN 80.0 3.3 AL
Dean 32 Gray Cheese NaN 1.8 AK
Christina 28 Black Melon NaN 9.5 TX
Cornelia 39 Red Beans NaN 2.2 TX
33在 DataFrame 末尾添加额外的行
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("\n------------ BEFORE ----------------\n")
print(employees)
employees.loc[len(employees)] = [45, '2018-01-25', 'Emp006', 'Sunny',
'Programmer']
print("\n------------ AFTER ----------------\n")
print(employees)
Output:
------------ BEFORE ----------------
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
------------ AFTER ----------------
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
5 45 2018-01-25 Emp006 Sunny Programmer
34为指定索引添加新行
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("\n------------ BEFORE ----------------\n")
print(employees)
employees.loc['Emp003'] = ['Sunny', 'Programmer', '2018-01-25', 45]
print("\n------------ AFTER ----------------\n")
print(employees)
Output:
------------ BEFORE ----------------
Name Occupation Date Of Join Age
Emp001 John Doe Chemist 2018-01-25 23
Emp002 William Spark Statistician 2018-01-26 24
------------ AFTER ----------------
Name Occupation Date Of Join Age
Emp001 John Doe Chemist 2018-01-25 23
Emp002 William Spark Statistician 2018-01-26 24
Emp003 Sunny Programmer 2018-01-25 45
35如何使用 for 循环添加行
import pandas as pd
cols = ['Zip']
lst = []
zip = 32100
for a in range(10):
lst.append([zip])
zip = zip + 1
df = pd.DataFrame(lst, columns=cols)
print(df)
Output:
Zip
0 32100
1 32101
2 32102
3 32103
4 32104
5 32105
6 32106
7 32107
8 32108
9 32109
36在 DataFrame 顶部添加一行
import pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp002', 'Emp003', 'Emp004'],
'Name': ['John', 'Doe', 'William'],
'Occupation': ['Chemist', 'Statistician', 'Statistician'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26'],
'Age': [23, 24, 34]})
print("\n------------ BEFORE ----------------\n")
print(employees)
# New line
line = pd.DataFrame({'Name': 'Dean', 'Age': 45, 'EmpCode': 'Emp001',
'Date Of Join': '2018-02-26', 'Occupation': 'Chemist'
}, index=[0])
# Concatenate two dataframe
employees = pd.concat([line,employees.ix[:]]).reset_index(drop=True)
print("\n------------ AFTER ----------------\n")
print(employees)
Output:
------------ BEFORE ----------------
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp002 John Chemist
1 24 2018-01-26 Emp003 Doe Statistician
2 34 2018-01-26 Emp004 William Statistician
------------ AFTER ----------------
Age Date Of Join EmpCode Name Occupation
0 45 2018-02-26 Emp001 Dean Chemist
1 23 2018-01-25 Emp002 John Chemist
2 24 2018-01-26 Emp003 Doe Statistician
3 34 2018-01-26 Emp004 William Statistician
37如何向 DataFrame 中动态添加行
import pandas as pd
df = pd.DataFrame(columns=['Name', 'Age'])
df.loc[1, 'Name'] = 'Rocky'
df.loc[1, 'Age'] = 23
df.loc[2, 'Name'] = 'Sunny'
print(df)
Output:
Name Age
1 Rocky 23
2 Sunny NaN
38在任意位置插入行
import pandas as pd
df = pd.DataFrame(columns=['Name', 'Age'])
df.loc[1, 'Name'] = 'Rocky'
df.loc[1, 'Age'] = 21
df.loc[2, 'Name'] = 'Sunny'
df.loc[2, 'Age'] = 22
df.loc[3, 'Name'] = 'Mark'
df.loc[3, 'Age'] = 25
df.loc[4, 'Name'] = 'Taylor'
df.loc[4, 'Age'] = 28
print("\n------------ BEFORE ----------------\n")
print(df)
line = pd.DataFrame({"Name": "Jack", "Age": 24}, index=[2.5])
df = df.append(line, ignore_index=False)
df = df.sort_index().reset_index(drop=True)
df = df.reindex(['Name', 'Age'], axis=1)
print("\n------------ AFTER ----------------\n")
print(df)
Output:
------------ BEFORE ----------------
Name Age
1 Rocky 21
2 Sunny 22
3 Mark 25
4 Taylor 28
------------ AFTER ----------------
Name Age
0 Rocky 21
1 Sunny 22
2 Jack 24
3 Mark 25
4 Taylor 28
39使用时间戳索引向 DataFrame 中添加行
import pandas as pd
df = pd.DataFrame(columns=['Name', 'Age'])
df.loc['2014-05-01 18:47:05', 'Name'] = 'Rocky'
df.loc['2014-05-01 18:47:05', 'Age'] = 21
df.loc['2014-05-02 18:47:05', 'Name'] = 'Sunny'
df.loc['2014-05-02 18:47:05', 'Age'] = 22
df.loc['2014-05-03 18:47:05', 'Name'] = 'Mark'
df.loc['2014-05-03 18:47:05', 'Age'] = 25
print("\n------------ BEFORE ----------------\n")
print(df)
line = pd.to_datetime("2014-05-01 18:50:05", format="%Y-%m-%d %H:%M:%S")
new_row = pd.DataFrame([['Bunny', 26]], columns=['Name', 'Age'], index=[line])
df = pd.concat([df, pd.DataFrame(new_row)], ignore_index=False)
print("\n------------ AFTER ----------------\n")
print(df)
Output:
------------ BEFORE ----------------
Name Age
2014-05-01 18:47:05 Rocky 21
2014-05-02 18:47:05 Sunny 22
2014-05-03 18:47:05 Mark 25
------------ AFTER ----------------
Name Age
2014-05-01 18:47:05 Rocky 21
2014-05-02 18:47:05 Sunny 22
2014-05-03 18:47:05 Mark 25
2014-05-01 18:50:05 Bunny 26
40为不同的行填充缺失值
import pandas as pd
a = {'A': 10, 'B': 20}
b = {'B': 30, 'C': 40, 'D': 50}
df1 = pd.DataFrame(a, index=[0])
df2 = pd.DataFrame(b, index=[1])
df = pd.DataFrame()
df = df.append(df1)
df = df.append(df2).fillna(0)
print(df)
Output:
A B C D
0 10.0 20 0.0 0.0
1 0.0 30 40.0 50.0
41append, concat 和 combine_first 示例
import pandas as pd
a = {'A': 10, 'B': 20}
b = {'B': 30, 'C': 40, 'D': 50}
df1 = pd.DataFrame(a, index=[0])
df2 = pd.DataFrame(b, index=[1])
d1 = pd.DataFrame()
d1 = d1.append(df1)
d1 = d1.append(df2).fillna(0)
print("\n------------ append ----------------\n")
print(d1)
d2 = pd.concat([df1, df2]).fillna(0)
print("\n------------ concat ----------------\n")
print(d2)
d3 = pd.DataFrame()
d3 = d3.combine_first(df1).combine_first(df2).fillna(0)
print("\n------------ combine_first ----------------\n")
print(d3)
Output:
------------ append ----------------
A B C D
0 10.0 20 0.0 0.0
1 0.0 30 40.0 50.0
------------ concat ----------------
A B C D
0 10.0 20 0.0 0.0
1 0.0 30 40.0 50.0
------------ combine_first ----------------
A B C D
0 10.0 20.0 0.0 0.0
1 0.0 30.0 40.0 50.0
42获取行和列的平均值
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
df['Mean Basket'] = df.mean(axis=1)
df.loc['Mean Fruit'] = df.mean()
print(df)
Output:
Apple Orange Banana Pear Mean Basket
Basket1 10.000000 20.0 30.0 40.000000 25.0
Basket2 7.000000 14.0 21.0 28.000000 17.5
Basket3 5.000000 5.0 0.0 0.000000 2.5
Mean Fruit 7.333333 13.0 17.0 22.666667 15.0
43计算行和列的总和
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
df['Sum Basket'] = df.sum(axis=1)
df.loc['Sum Fruit'] = df.sum()
print(df)
Output:
Apple Orange Banana Pear Sum Basket
Basket1 10 20 30 40 100
Basket2 7 14 21 28 70
Basket3 5 5 0 0 10
Sum Fruit 22 39 51 68 180
44连接两列
import pandas as pd
df = pd.DataFrame(columns=['Name', 'Age'])
df.loc[1, 'Name'] = 'Rocky'
df.loc[1, 'Age'] = 21
df.loc[2, 'Name'] = 'Sunny'
df.loc[2, 'Age'] = 22
df.loc[3, 'Name'] = 'Mark'
df.loc[3, 'Age'] = 25
df.loc[4, 'Name'] = 'Taylor'
df.loc[4, 'Age'] = 28
print('\n------------ BEFORE ----------------\n')
print(df)
df['Employee'] = df['Name'].map(str) + ' - ' + df['Age'].map(str)
df = df.reindex(['Employee'], axis=1)
print('\n------------ AFTER ----------------\n')
print(df)
Output:
------------ BEFORE ----------------
Name Age
1 Rocky 21
2 Sunny 22
3 Mark 25
4 Taylor 28
------------ AFTER ----------------
Employee
1 Rocky - 21
2 Sunny - 22
3 Mark - 25
4 Taylor - 28
45过滤包含某字符串的行
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df)
print("\n---- Filter with State contains TX ----\n")
df1 = df[df['State'].str.contains("TX")]
print(df1)
Output:
DateOfBirth State
Jane 1986-11-11 NY
Nick 1999-05-12 TX
Aaron 1976-01-01 FL
Penelope 1986-06-01 AL
Dean 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
---- Filter with State contains TX ----
DateOfBirth State
Nick 1999-05-12 TX
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
46过滤索引中包含某字符串的行
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
print(df)
print("\n---- Filter Index contains ane ----\n")
df.index = df.index.astype('str')
df1 = df[df.index.str.contains('ane')]
print(df1)
Output:
DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Aaron 1976-01-01 FL
Penelope 1986-06-01 AL
Frane 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
---- Filter Index contains ane ----
DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Frane 1983-06-04 AK
47使用 AND 运算符过滤包含特定字符串值的行
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
print(df)
print("\n---- Filter DataFrame using & ----\n")
df.index = df.index.astype('str')
df1 = df[df.index.str.contains('ane') & df['State'].str.contains("TX")]
print(df1)
Output:
DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Aaron 1976-01-01 FL
Penelope 1986-06-01 AL
Frane 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
---- Filter DataFrame using & ----
DateOfBirth State
Pane 1999-05-12 TX
48查找包含某字符串的所有行
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
print(df)
print("\n---- Filter DataFrame using & ----\n")
df.index = df.index.astype('str')
df1 = df[df.index.str.contains('ane') | df['State'].str.contains("TX")]
print(df1)
Output:
DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Aaron 1976-01-01 FL
Penelope 1986-06-01 AL
Frane 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
---- Filter DataFrame using & ----
DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Frane 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
49如果行中的值包含字符串，则创建与字符串相等的另一列
import pandas as pd
import numpy as np
df = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Accountant', '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['Department'] = pd.np.where(df.Occupation.str.contains("Chemist"), "Science",
pd.np.where(df.Occupation.str.contains("Statistician"), "Economics",
pd.np.where(df.Occupation.str.contains("Programmer"), "Computer", "General")))
print(df)
Output:
Age Date Of Join EmpCode Name Occupation Department
0 23 2018-01-25 Emp001 John Chemist Science
1 24 2018-01-26 Emp002 Doe Accountant General
2 34 2018-01-26 Emp003 William Statistician Economics
3 29 2018-02-26 Emp004 Spark Statistician Economics
4 40 2018-03-16 Emp005 Mark Programmer Computer
50计算 pandas group 中每组的行数
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0],
[6, 6, 6, 6], [8, 8, 8, 8], [5, 5, 0, 0]],
columns=['Apple', 'Orange', 'Rice', 'Oil'],
index=['Basket1', 'Basket2', 'Basket3',
'Basket4', 'Basket5', 'Basket6'])
print(df)
print("\n ----------------------------- \n")
print(df[['Apple', 'Orange', 'Rice', 'Oil']].
groupby(['Apple']).agg(['mean', 'count']))
Output:
Apple Orange Rice Oil
Basket1 10 20 30 40
Basket2 7 14 21 28
Basket3 5 5 0 0
Basket4 6 6 6 6
Basket5 8 8 8 8
Basket6 5 5 0 0
-----------------------------
Orange Rice Oil
mean count mean count mean count
Apple
5 5 2 0 2 0 2
6 6 1 6 1 6 1
7 14 1 21 1 28 1
8 8 1 8 1 8 1
10 20 1 30 1 40 1
51检查字符串是否在 DataFrme 中
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
if df['State'].str.contains('TX').any():
print("TX is there")
Output:
TX is there
52从 DataFrame 列中获取唯一行值
import pandas as pd
df = pd.DataFrame({'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df)
print("\n----------------\n")
print(df["State"].unique())
Output:
State
Jane NY
Nick TX
Aaron FL
Penelope AL
Dean AK
Christina TX
Cornelia TX
----------------
['NY' 'TX' 'FL' 'AL' 'AK']
53计算 DataFrame 列的不同值
import pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 20, 30, 20, 25],
'Height': [165, 70, 120, 80, 162, 72, 124, 81],
'Score': [4.6, 8.3, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'TX', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Jaane', 'Nicky', 'Armour', 'Ponting'])
print(df.Age.value_counts())
Output:
20 3
30 2
25 1
22 1
40 1
Name: Age, dtype: int64
54删除具有重复索引的行
import pandas as pd
df = pd.DataFrame({'Age': [30, 30, 22, 40, 20, 30, 20, 25],
'Height': [165, 165, 120, 80, 162, 72, 124, 81],
'Score': [4.6, 4.6, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'NY', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print("\n -------- Duplicate Rows ----------- \n")
print(df)
df1 = df.reset_index().drop_duplicates(subset='index',
keep='first').set_index('index')
print("\n ------- Unique Rows ------------ \n")
print(df1)
Output:
-------- Duplicate Rows -----------
Age Height Score State
Jane 30 165 4.6 NY
Jane 30 165 4.6 NY
Aaron 22 120 9.0 FL
Penelope 40 80 3.3 AL
Jaane 20 162 4.0 NY
Nicky 30 72 8.0 TX
Armour 20 124 9.0 FL
Ponting 25 81 3.0 AL
------- Unique Rows ------------
Age Height Score State
index
Jane 30 165 4.6 NY
Aaron 22 120 9.0 FL
Penelope 40 80 3.3 AL
Jaane 20 162 4.0 NY
Nicky 30 72 8.0 TX
Armour 20 124 9.0 FL
Ponting 25 81 3.0 AL
55删除某些列具有重复值的行
import pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81],
'Score': [4.6, 4.6, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'NY', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print("\n -------- Duplicate Rows ----------- \n")
print(df)
df1 = df.reset_index().drop_duplicates(subset=['Age','Height'],
keep='first').set_index('index')
print("\n ------- Unique Rows ------------ \n")
print(df1)
Output:
-------- Duplicate Rows -----------
Age Height Score State
Jane 30 120 4.6 NY
Jane 40 162 4.6 NY
Aaron 30 120 9.0 FL
Penelope 40 120 3.3 AL
Jaane 30 120 4.0 NY
Nicky 30 72 8.0 TX
Armour 20 120 9.0 FL
Ponting 25 81 3.0 AL
------- Unique Rows ------------
Age Height Score State
index
Jane 30 120 4.6 NY
Jane 40 162 4.6 NY
Penelope 40 120 3.3 AL
Nicky 30 72 8.0 TX
Armour 20 120 9.0 FL
Ponting 25 81 3.0 AL
56从 DataFrame 单元格中获取值
import pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81],
'Score': [4.6, 4.6, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'NY', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print(df.loc['Nicky', 'Age'])
Output:
30
57使用 DataFrame 中的条件索引获取单元格上的标量值
import pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81],
'Score': [4.6, 4.6, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'NY', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print("\nGet Height where Age is 20")
print(df.loc[df['Age'] == 20, 'Height'].values[0])
print("\nGet State where Age is 30")
print(df.loc[df['Age'] == 30, 'State'].values[0])
Output:
Get Height where Age is 20
120
Get State where Age is 30
NY
58设置 DataFrame 的特定单元格值
import pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81]},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print("\n--------------Before------------\n")
print(df)
df.iat[0, 0] = 90
df.iat[0, 1] = 91
df.iat[1, 1] = 92
df.iat[2, 1] = 93
df.iat[7, 1] = 99
print("\n--------------After------------\n")
print(df)
Output:
--------------Before------------
Age Height
Jane 30 120
Jane 40 162
Aaron 30 120
Penelope 40 120
Jaane 30 120
Nicky 30 72
Armour 20 120
Ponting 25 81
--------------After------------
Age Height
Jane 90 91
Jane 40 92
Aaron 30 93
Penelope 40 120
Jaane 30 120
Nicky 30 72
Armour 20 120
Ponting 25 99
59从 DataFrame 行获取单元格值
import pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81]},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print(df.loc[df.Age == 30,'Height'].tolist())
Output:
[120, 120, 120, 72]
60用字典替换 DataFrame 列中的值
import pandas as pd
df = pd.DataFrame({'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df)
dict = {"NY": 1, "TX": 2, "FL": 3, "AL": 4, "AK": 5}
df1 = df.replace({"State": dict})
print("\n\n")
print(df1)
Output:
State
Jane NY
Nick TX
Aaron FL
Penelope AL
Dean AK
Christina TX
Cornelia TX
State
Jane 1
Nick 2
Aaron 3
Penelope 4
Dean 5
Christina 2
Cornelia 2
61统计基于某一列的一列的数值
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df.groupby('State').DateOfBirth.nunique())
Output:
State
AK 1
AL 1
FL 1
NY 1
TX 3
Name: DateOfBirth, dtype: int64
62处理 DataFrame 中的缺失值
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5,]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print("\n--------- DataFrame ---------\n")
print(df)
print("\n--------- Use of isnull() ---------\n")
print(df.isnull())
print("\n--------- Use of notnull() ---------\n")
print(df.notnull())
Output:
--------- DataFrame ---------
Apple Orange Banana Pear
Basket1 10 20.0 30.0 40.0
Basket2 7 14.0 21.0 28.0
Basket3 5 NaN NaN NaN
--------- Use of isnull() ---------
Apple Orange Banana Pear
Basket1 False False False False
Basket2 False False False False
Basket3 False True True True
--------- Use of notnull() ---------
Apple Orange Banana Pear
Basket1 True True True True
Basket2 True True True True
Basket3 True False False False
63删除包含任何缺失数据的行
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5,]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print("\n--------- DataFrame ---------\n")
print(df)
print("\n--------- Use of dropna() ---------\n")
print(df.dropna())
Output:
--------- DataFrame ---------
Apple Orange Banana Pear
Basket1 10 20.0 30.0 40.0
Basket2 7 14.0 21.0 28.0
Basket3 5 NaN NaN NaN
--------- Use of dropna() ---------
Apple Orange Banana Pear
Basket1 10 20.0 30.0 40.0
Basket2 7 14.0 21.0 28.0
64删除 DataFrame 中缺失数据的列
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5,]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print("\n--------- DataFrame ---------\n")
print(df)
print("\n--------- Drop Columns) ---------\n")
print(df.dropna(1))
Output:
--------- DataFrame ---------
Apple Orange Banana Pear
Basket1 10 20.0 30.0 40.0
Basket2 7 14.0 21.0 28.0
Basket3 5 NaN NaN NaN
--------- Drop Columns) ---------
Apple
Basket1 10
Basket2 7
Basket3 5
65按降序对索引值进行排序
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
print(df.sort_index(ascending=False))
Output:
DateOfBirth State
Penelope 1986-06-01 AL
Pane 1999-05-12 TX
Jane 1986-11-11 NY
Frane 1983-06-04 AK
Cornelia 1999-07-09 TX
Christina 1990-03-07 TX
Aaron 1976-01-01 FL
66按降序对列进行排序
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.sort_index(axis=1, ascending=False))
Output:
Occupation Name EmpCode Date Of Join Age
0 Chemist John Emp001 2018-01-25 23
1 Statistician Doe Emp002 2018-01-26 24
2 Statistician William Emp003 2018-01-26 34
3 Statistician Spark Emp004 2018-02-26 29
4 Programmer Mark Emp005 2018-03-16 40
67使用 rank 方法查找 DataFrame 中元素的排名
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print("\n--------- DataFrame Values--------\n")
print(df)
print("\n--------- DataFrame Values by Rank--------\n")
print(df.rank())
Output:
--------- DataFrame Values--------
Apple Orange Banana Pear
Basket1 10 20 30 40
Basket2 7 14 21 28
Basket3 5 5 0 0
--------- DataFrame Values by Rank--------
Apple Orange Banana Pear
Basket1 3.0 3.0 3.0 3.0
Basket2 2.0 2.0 2.0 2.0
Basket3 1.0 1.0 1.0 1.0
68在多列上设置索引
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 --------- Before Index ----------- \n")
print(employees)
print("\n --------- Multiple Indexing ----------- \n")
print(employees.set_index(['Occupation', 'Age']))
Output:
Date Of Join EmpCode Name
Occupation Age
Chemist 23 2018-01-25 Emp001 John
Statistician 24 2018-01-26 Emp002 Doe
34 2018-01-26 Emp003 William
29 2018-02-26 Emp004 Spark
Programmer 40 2018-03-16 Emp005 Mark
69确定 DataFrame 的周期索引和列
import pandas as pd
values = ["India", "Canada", "Australia",
"Japan", "Germany", "France"]
pidx = pd.period_range('2015-01-01', periods=6)
df = pd.DataFrame(values, index=pidx, columns=['Country'])
print(df)
Output:
Country
2015-01-01 India
2015-01-02 Canada
2015-01-03 Australia
2015-01-04 Japan
2015-01-05 Germany
2015-01-06 France
70导入 CSV 指定特定索引
import pandas as pd
df = pd.read_csv('test.csv', index_col="DateTime")
print(df)
Output:
Wheat Rice Oil
DateTime
10/10/2016 10.500 12.500 16.500
10/11/2016 11.250 12.750 17.150
10/12/2016 10.000 13.150 15.500
10/13/2016 12.000 14.500 16.100
10/14/2016 13.000 14.825 15.600
10/15/2016 13.075 15.465 15.315
10/16/2016 13.650 16.105 15.030
10/17/2016 14.225 16.745 14.745
10/18/2016 14.800 17.385 14.460
10/19/2016 15.375 18.025 14.175
71将 DataFrame 写入 csv
import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
df.to_csv('test.csv', encoding='utf-8', index=True)
Output:
检查本地文件
72使用 Pandas 读取 csv 文件的特定列
import pandas as pd
df = pd.read_csv("test.csv", usecols = ['Wheat','Oil'])
print(df)
73Pandas 获取 CSV 列的列表
import pandas as pd
cols = list(pd.read_csv("test.csv", nrows =1))
print(cols)
Output:
['DateTime', 'Wheat', 'Rice', 'Oil']
74找到列值最大的行
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print(df.ix[df['Apple'].idxmax()])
Output:
Apple 55
Orange 15
Banana 8
Pear 12
Name: Basket3, dtype: int64
75使用查询方法进行复杂条件选择
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print(df)
print("\n ----------- Filter data using query method ------------- \n")
df1 = df.ix[df.query('Apple > 50 & Orange <= 15 & Banana < 15 & Pear == 12').index]
print(df1)
Output:
Apple Orange Banana Pear
Basket1 10 20 30 40
Basket2 7 14 21 28
Basket3 55 15 8 12
----------- Filter data using query method -------------
Apple Orange Banana Pear
Basket3 55 15 8 12
76检查 Pandas 中是否存在列
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
if 'Apple' in df.columns:
print("Yes")
else:
print("No")
if set(['Apple','Orange']).issubset(df.columns):
print("Yes")
else:
print("No")
77为特定列从 DataFrame 中查找 n-smallest 和 n-largest 值
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- nsmallest -----------\n")
print(df.nsmallest(2, ['Apple']))
print("\n----------- nlargest -----------\n")
print(df.nlargest(2, ['Apple']))
Output:
----------- nsmallest -----------
Apple Orange Banana Pear
Basket6 5 4 9 2
Basket2 7 14 21 28
----------- nlargest -----------
Apple Orange Banana Pear
Basket3 55 15 8 12
Basket4 15 14 1 8
78从 DataFrame 中查找所有列的最小值和最大值
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Minimum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].min())
print("\n----------- Maximum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].max())
Output:
----------- Minimum -----------
Apple 5
Orange 1
Banana 1
Pear 2
dtype: int64
----------- Maximum -----------
Apple 55
Orange 20
Banana 30
Pear 40
dtype: int64
79在 DataFrame 中找到最小值和最大值所在的索引位置
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Minimum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].idxmin())
print("\n----------- Maximum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].idxmax())
Output:
----------- Minimum -----------
Apple Basket6
Orange Basket5
Banana Basket4
Pear Basket6
dtype: object
----------- Maximum -----------
Apple Basket3
Orange Basket1
Banana Basket1
Pear Basket1
dtype: object
80计算 DataFrame Columns 的累积乘积和累积总和
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Cumulative Product -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].cumprod())
print("\n----------- Cumulative Sum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].cumsum())
Output:
----------- Cumulative Product -----------
Apple Orange Banana Pear
Basket1 10 20 30 40
Basket2 70 280 630 1120
Basket3 3850 4200 5040 13440
Basket4 57750 58800 5040 107520
Basket5 404250 58800 5040 860160
Basket6 2021250 235200 45360 1720320
----------- Cumulative Sum -----------
Apple Orange Banana Pear
Basket1 10 20 30 40
Basket2 17 34 51 68
Basket3 72 49 59 80
Basket4 87 63 60 88
Basket5 94 64 61 96
Basket6 99 68 70 98
81汇总统计
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Describe DataFrame -----------\n")
print(df.describe())
print("\n----------- Describe Column -----------\n")
print(df[['Apple']].describe())
Output:
----------- Describe DataFrame -----------
Apple Orange Banana Pear
count 6.000000 6.000000 6.000000 6.000000
mean 16.500000 11.333333 11.666667 16.333333
std 19.180719 7.257180 11.587349 14.555640
min 5.000000 1.000000 1.000000 2.000000
25% 7.000000 6.500000 2.750000 8.000000
50% 8.500000 14.000000 8.500000 10.000000
75% 13.750000 14.750000 18.000000 24.000000
max 55.000000 20.000000 30.000000 40.000000
----------- Describe Column -----------
Apple
count 6.000000
mean 16.500000
std 19.180719
min 5.000000
25% 7.000000
50% 8.500000
75% 13.750000
max 55.000000
82查找 DataFrame 的均值、中值和众数
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Calculate Mean -----------\n")
print(df.mean())
print("\n----------- Calculate Median -----------\n")
print(df.median())
print("\n----------- Calculate Mode -----------\n")
print(df.mode())
Output:
----------- Calculate Mean -----------
Apple 16.500000
Orange 11.333333
Banana 11.666667
Pear 16.333333
dtype: float64
----------- Calculate Median -----------
Apple 8.5
Orange 14.0
Banana 8.5
Pear 10.0
dtype: float64
----------- Calculate Mode -----------
Apple Orange Banana Pear
0 7 14 1 8
83测量 DataFrame 列的方差和标准偏差
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Calculate Mean -----------\n")
print(df.mean())
print("\n----------- Calculate Median -----------\n")
print(df.median())
print("\n----------- Calculate Mode -----------\n")
print(df.mode())
Output:
----------- Measure Variance -----------
Apple 367.900000
Orange 52.666667
Banana 134.266667
Pear 211.866667
dtype: float64
----------- Standard Deviation -----------
Apple 19.180719
Orange 7.257180
Banana 11.587349
Pear 14.555640
dtype: float64
84计算 DataFrame 列之间的协方差
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Calculating Covariance -----------\n")
print(df.cov())
print("\n----------- Between 2 columns -----------\n")
# Covariance of Apple vs Orange
print(df.Apple.cov(df.Orange))
Output:
----------- Calculating Covariance -----------
Apple Orange Banana Pear
Apple 367.9 47.600000 -40.200000 -35.000000
Orange 47.6 52.666667 54.333333 77.866667
Banana -40.2 54.333333 134.266667 154.933333
Pear -35.0 77.866667 154.933333 211.866667
----------- Between 2 columns -----------
47.60000000000001
85计算 Pandas 中两个 DataFrame 对象之间的相关性
import pandas as pd
df1 = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ Calculating Correlation of one DataFrame Columns -----\n")
print(df1.corr())
df2 = pd.DataFrame([[52, 54, 58, 41], [14, 24, 51, 78], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 17, 18, 98], [15, 34, 29, 52]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----- Calculating correlation between two DataFrame -------\n")
print(df2.corrwith(other=df1))
Output:
------ Calculating Correlation of one DataFrame Columns -----
Apple Orange Banana Pear
Apple 1.000000 0.341959 -0.180874 -0.125364
Orange 0.341959 1.000000 0.646122 0.737144
Banana -0.180874 0.646122 1.000000 0.918606
Pear -0.125364 0.737144 0.918606 1.000000
----- Calculating correlation between two DataFrame -------
Apple 0.678775
Orange 0.354993
Banana 0.920872
Pear 0.076919
dtype: float64
86计算 DataFrame 列的每个单元格的百分比变化
import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ Percent change at each cell of a Column -----\n")
print(df[['Apple']].pct_change()[:3])
print("\n------ Percent change at each cell of a DataFrame -----\n")
print(df.pct_change()[:5])
Output:
------ Percent change at each cell of a Column -----
Apple
Basket1 NaN
Basket2 -0.300000
Basket3 6.857143
------ Percent change at each cell of a DataFrame -----
Apple Orange Banana Pear
Basket1 NaN NaN NaN NaN
Basket2 -0.300000 -0.300000 -0.300000 -0.300000
Basket3 6.857143 0.071429 -0.619048 -0.571429
Basket4 -0.727273 -0.066667 -0.875000 -0.333333
Basket5 -0.533333 -0.928571 0.000000 0.000000
87在 Pandas 中向前和向后填充 DataFrame 列的缺失值
import pandas as pd
df = pd.DataFrame([[10, 30, 40], [], [15, 8, 12],
[15, 14, 1, 8], [7, 8], [5, 4, 1]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ DataFrame with NaN -----\n")
print(df)
print("\n------ DataFrame with Forward Filling -----\n")
print(df.ffill())
print("\n------ DataFrame with Forward Filling -----\n")
print(df.bfill())
Output:
------ DataFrame with NaN -----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 NaN
Basket2 NaN NaN NaN NaN
Basket3 15.0 8.0 12.0 NaN
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 NaN NaN
Basket6 5.0 4.0 1.0 NaN
------ DataFrame with Forward Filling -----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 NaN
Basket2 10.0 30.0 40.0 NaN
Basket3 15.0 8.0 12.0 NaN
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 1.0 8.0
Basket6 5.0 4.0 1.0 8.0
------ DataFrame with Forward Filling -----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 8.0
Basket2 15.0 8.0 12.0 8.0
Basket3 15.0 8.0 12.0 8.0
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 1.0 NaN
Basket6 5.0 4.0 1.0 NaN
88在 Pandas 中使用非分层索引使用 Stacking
import pandas as pd
df = pd.DataFrame([[10, 30, 40], [], [15, 8, 12],
[15, 14, 1, 8], [7, 8], [5, 4, 1]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ DataFrame-----\n")
print(df)
print("\n------ Stacking DataFrame -----\n")
print(df.stack(level=-1))
Output:
------ DataFrame-----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 NaN
Basket2 NaN NaN NaN NaN
Basket3 15.0 8.0 12.0 NaN
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 NaN NaN
Basket6 5.0 4.0 1.0 NaN
------ Stacking DataFrame -----
Basket1 Apple 10.0
Orange 30.0
Banana 40.0
Basket3 Apple 15.0
Orange 8.0
Banana 12.0
Basket4 Apple 15.0
Orange 14.0
Banana 1.0
Pear 8.0
Basket5 Apple 7.0
Orange 8.0
Basket6 Apple 5.0
Orange 4.0
Banana 1.0
dtype: float64
89使用分层索引对 Pandas 进行拆分
import pandas as pd
df = pd.DataFrame([[10, 30, 40], [], [15, 8, 12],
[15, 14, 1, 8], [7, 8], [5, 4, 1]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ DataFrame-----\n")
print(df)
print("\n------ Unstacking DataFrame -----\n")
print(df.unstack(level=-1))
Output:
------ DataFrame-----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 NaN
Basket2 NaN NaN NaN NaN
Basket3 15.0 8.0 12.0 NaN
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 NaN NaN
Basket6 5.0 4.0 1.0 NaN
------ Unstacking DataFrame -----
Apple Basket1 10.0
Basket2 NaN
Basket3 15.0
Basket4 15.0
Basket5 7.0
Basket6 5.0
Orange Basket1 30.0
Basket2 NaN
Basket3 8.0
Basket4 14.0
Basket5 8.0
Basket6 4.0
Banana Basket1 40.0
Basket2 NaN
Basket3 12.0
Basket4 1.0
Basket5 NaN
Basket6 1.0
Pear Basket1 NaN
Basket2 NaN
Basket3 NaN
Basket4 8.0
Basket5 NaN
Basket6 NaN
dtype: float64
90Pandas 获取 HTML 页面上 table 数据
import pandas as pd
df pd.read_html("url")
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