MySQL入门整理

皮大大

发布于 2021-03-01 15:31:27

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文章被收录于专栏：机器学习/数据可视化机器学习/数据可视化

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代码可运行

Pandas操作技巧

pandas中有很多的操作技巧需要我们去挖掘，本文中介绍pandas中的3种操作奇技淫巧，让pandas的操作骚动起来。

剪贴板中的数据生成DF型数据
通过数据类型直接选择columns
将strings转成numbers

导入库

import pandas as pd
import numpy as np

读取excel表格

通过read_excel()函数

df1 = pd.read_excel("salesfunnel.xlsx")
df1

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	Account	Name	Rep	Manager	Product	Quantity	Price	Status
0	714466	Trantow-Barrows	Craig Booker	Debra Henley	CPU	1	30000	presented
1	714466	Trantow-Barrows	Craig Booker	Debra Henley	Software	1	10000	presented
2	714466	Trantow-Barrows	Craig Booker	Debra Henley	Maintenance	2	5000	pending
3	737550	Fritsch, Russel and Anderson	Craig Booker	Debra Henley	CPU	1	35000	declined
4	146832	Kiehn-Spinka	Daniel Hilton	Debra Henley	CPU	2	65000	won
5	218895	Kulas Inc	Daniel Hilton	Debra Henley	CPU	2	40000	pending
6	218895	Kulas Inc	Daniel Hilton	Debra Henley	Software	1	10000	presented
7	412290	Jerde-Hilpert	John Smith	Debra Henley	Maintenance	2	5000	pending
8	740150	Barton LLC	John Smith	Debra Henley	CPU	1	35000	declined
9	141962	Herman LLC	Cedric Moss	Fred Anderson	CPU	2	65000	won
10	163416	Purdy-Kunde	Cedric Moss	Fred Anderson	CPU	1	30000	presented
11	239344	Stokes LLC	Cedric Moss	Fred Anderson	Maintenance	1	5000	pending
12	239344	Stokes LLC	Cedric Moss	Fred Anderson	Software	1	10000	presented
13	307599	Kassulke, Ondricka and Metz	Wendy Yule	Fred Anderson	Maintenance	3	7000	won
14	688981	Keeling LLC	Wendy Yule	Fred Anderson	CPU	5	100000	won
15	729833	Koepp Ltd	Wendy Yule	Fred Anderson	CPU	2	65000	declined
16	729833	Koepp Ltd	Wendy Yule	Fred Anderson	Monitor	2	5000	presented

通过read_clipboard()

这个方法是将我们剪贴板中的内容直接变成DataFrame型数据，不需要其他转换。

我们需要事先在表格中剪贴好数据，然后直接运行下面的代码：

# 现在剪贴板中进行赋值，再执行下面的语句

df2 = pd.read_clipboard()
df2

.dataframe tbody tr th:only-of-type { vertical-align: middle; } <pre><code>.dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </code></pre>

	Account	Name	Rep	Manager	Product	Quantity	Price	Status
0	714466	Trantow-Barrows	Craig Booker	Debra Henley	CPU	1	30000	presented
1	714466	Trantow-Barrows	Craig Booker	Debra Henley	Software	1	10000	presented
2	714466	Trantow-Barrows	Craig Booker	Debra Henley	Maintenance	2	5000	pending
3	737550	Fritsch, Russel and Anderson	Craig Booker	Debra Henley	CPU	1	35000	declined
4	146832	Kiehn-Spinka	Daniel Hilton	Debra Henley	CPU	2	65000	won
5	218895	Kulas Inc	Daniel Hilton	Debra Henley	CPU	2	40000	pending
6	218895	Kulas Inc	Daniel Hilton	Debra Henley	Software	1	10000	presented
7	412290	Jerde-Hilpert	John Smith	Debra Henley	Maintenance	2	5000	pending
8	740150	Barton LLC	John Smith	Debra Henley	CPU	1	35000	declined
9	141962	Herman LLC	Cedric Moss	Fred Anderson	CPU	2	65000	won
10	163416	Purdy-Kunde	Cedric Moss	Fred Anderson	CPU	1	30000	presented
11	239344	Stokes LLC	Cedric Moss	Fred Anderson	Maintenance	1	5000	pending
12	239344	Stokes LLC	Cedric Moss	Fred Anderson	Software	1	10000	presented
13	307599	Kassulke, Ondricka and Metz	Wendy Yule	Fred Anderson	Maintenance	3	7000	won
14	688981	Keeling LLC	Wendy Yule	Fred Anderson	CPU	5	100000	won
15	729833	Koepp Ltd	Wendy Yule	Fred Anderson	CPU	2	65000	declined
16	729833	Koepp Ltd	Wendy Yule	Fred Anderson	Monitor	2	5000	presented

通过数据类型选择columns

df3 = pd.DataFrame({'name':['xiaoming','zhansan','lisi'],
                    'age':[22,28,25],
                    'address':['shenzhen','guangzhou','changsha'],
                    'height':[178,180,176]
                   })
df3

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	name	age	address	height
0	xiaoming	22	shenzhen	178
1	zhansan	28	guangzhou	180
2	lisi	25	changsha	176

df3.dtypes

name       object
age         int64
address    object
height      int64
dtype: object

# 我们直接选择int64型的数据

df3.select_dtypes(include='int64')

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	age	height
0	22	178
1	28	180
2	25	176

# 选择除了int64之外的数据

df3.select_dtypes(exclude='int64')

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	name	address
0	xiaoming	shenzhen
1	zhansan	guangzhou
2	lisi	changsha

# 同时选择多种数据类型

df3.select_dtypes(include=['int64','object'])

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	name	age	address	height
0	xiaoming	22	shenzhen	178
1	zhansan	28	guangzhou	180
2	lisi	25	changsha	176

将strings转成numbers

两种方法将字符串改成数值型数据

指定类型用：astype()
to_numeric()

df4 = pd.DataFrame({'goods':['A','B','C'],
                   'price':['30','20','60'],
                   'sales':['50','-','40']}
                  )
df4

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	goods	price	sales
0	A	30	50
1	B	20	-
2	C	60	40

df4.dtypes

goods    object
price    object
sales    object
dtype: object

# 将price转成int型

# 等价: df4 = df4.astype({'price': 'int'})
df4['price'] = df4['price'].astype('int64')
df4

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	goods	price	sales
0	A	30	50
1	B	20	-
2	C	60	40

df4.dtypes

goods    object
price     int64
sales    object
dtype: object

使用同样的方法转化sales列数据则会报错：

df4['sales'] = pd.to_numeric(df4['sales'], errors='coerce')
df4

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	goods	price	sales
0	A	30	50.0
1	B	20	NaN
2	C	60	40.0

df4.dtypes

goods     object
price      int64
sales    float64
dtype: object

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原始发表：2020-10-19，如有侵权请联系 cloudcommunity@tencent.com 删除

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