肝了3天，整理了90个Pandas案例，强烈建议收藏！
Posted 大数据v
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了肝了3天，整理了90个Pandas案例，强烈建议收藏！相关的知识，希望对你有一定的参考价值。
导读：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。
作者：周萝卜
来源：萝卜大杂烩（ID：luobodazahui）
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
01 如何使用列表和字典创建 Series
1. 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: float642. 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: object3. 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: object4. 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: object02 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: float6403 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 ]04 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: object05 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,)
606 如何获取 Series 开始或末尾几行数据
1. 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: object2. 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: object3. 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: object07 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: int6408 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 Statistician09 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 2410 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 Statistician11 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 Chemist12 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 Chemist13 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 -- 4014 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 Programmer15 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 Toronto16 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 3318 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 Mark19 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 TX20 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 AK21 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 France23 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 AL24 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）
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 17225 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 0 026 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 15027 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: object28 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: object29 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 TX30 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 TX31 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: object32 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 TX33 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 Programmer34 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 4535 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 3210936 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 3以上是关于肝了3天，整理了90个Pandas案例，强烈建议收藏！的主要内容，如果未能解决你的问题，请参考以下文章
肝了3天，整理了50个Pandas高频使用技巧，强烈建议收藏！
肝了3天，整理了65个Matplotlib案例，这能不收藏？