pandas用法总结_pandas distribute-优快云博客

本文链接：https://blog.youkuaiyun.com/llf000000/article/details/123171445

本文总结了如何使用Pandas从CSV和Excel文件创建数据表，查看表信息，清洗数据，预处理数据，以及数据合并的方法。涵盖了关键操作如填充空值、数据类型转换、列名修改等。

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pandas用法总结

一、生成数据表

1、首先导入pandas库，一般都会用到numpy库，所以我们先导入备用：

import numpy as np
import pandas as pd

2、导入CSV或者xlsx文件：

df = pd.DataFrame(pd.read_csv('name.csv',header=1))
df = pd.DataFrame(pd.read_excel('name.xlsx'))

或者

import pandas as pd
from collections import namedtuple

Item = namedtuple('Item', 'reply pv')
items = []

with codecs.open('reply.pv.07', 'r', 'utf-8') as f: 
    for line in f:
        line_split = line.strip().split('\t')
        items.append(Item(line_split[0].strip(), line_split[1].strip()))

df = pd.DataFrame.from_records(items, columns=['reply', 'pv'])

3、用pandas创建数据表：

df = pd.DataFrame({"id":[1001,1002,1003,1004,1005,1006], 
 "date":pd.date_range('20130102', periods=6),
  "city":['Beijing ', 'SH', ' guangzhou ', 'Shenzhen', 'shanghai', 'BEIJING '],
 "age":[23,44,54,32,34,32],
 "category":['100-A','100-B','110-A','110-C','210-A','130-F'],
  "price":[1200,np.nan,2133,5433,np.nan,4432]},
  columns =['id','date','city','category','age','price'])

4、创建过程合并

import numpy as np
import pandas as pd


df = pd.DataFrame({"id": [1001, 1002, 1003, 1004, 1005, 1006],
                   "date": pd.date_range('20130102', periods=6),
                   "city": ['Beijing ', 'SH', ' guangzhou ', 'Shenzhen', 'shanghai', 'BEIJING '],
                   "age": [23, 44, 54, 32, 34, 32],
                   "category": ['100-A', '100-B', '110-A', '110-C', '210-A', '130-F'],
                   "price": [1200, np.nan, 2133, 5433, np.nan, 4432]},
                  columns=['id', 'date', 'city', 'category', 'age', 'price'])

对比以下代码

import pandas

data = {
    "Day": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    "Visitors": [18, 26, 18, 18, 9, 9, 20, 30, 16, 24],
    "Bounce_Rate": [77.27, 74.07, 73.68, 65, 90, 70, 72, 62.16, 81.25, 72],
}

df = pandas.DataFrame(data)
print(df)

二、数据表信息查看

1、维度查看：

print(df.shape)# (6, 6)

2、数据表基本信息（维度、列名称、数据格式、所占空间等）：

print(df.info())
'''
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 6 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   id        6 non-null      int64         
 1   date      6 non-null      datetime64[ns]
 2   city      6 non-null      object        
 3   category  6 non-null      object        
 4   age       6 non-null      int64         
 5   price     4 non-null      float64       
dtypes: datetime64[ns](1), float64(1), int64(2), object(2)
memory usage: 416.0+ bytes
None
'''

3、每一列数据的格式：

print(df.dtypes)  # dtype: object

4、某一列格式：

df['B'].dtype

5、空值：

df.isnull()

6、查看某一列空值

df['B'].isnull()

7、查看某一列的唯一值：

df['B'].unique()

8、查看数据表的值：

print(df.values )
'''
[[1001 Timestamp('2013-01-02 00:00:00') 'Beijing ' '100-A' 23 1200.0]
 [1002 Timestamp('2013-01-03 00:00:00') 'SH' '100-B' 44 nan]
 [1003 Timestamp('2013-01-04 00:00:00') ' guangzhou ' '110-A' 54 2133.0]
 [1004 Timestamp('2013-01-05 00:00:00') 'Shenzhen' '110-C' 32 5433.0]
 [1005 Timestamp('2013-01-06 00:00:00') 'shanghai' '210-A' 34 nan]
 [1006 Timestamp('2013-01-07 00:00:00') 'BEIJING ' '130-F' 32 4432.0]]
 '''

9、查看列名称：

df.columns

10、查看前5行数据、后5行数据：

df.head() #默认前5行数据
df.tail()    #默认后5行数据

三、数据表清洗

1、用数字0填充空值：

df.fillna(value=0)

2、使用列prince的均值对NA进行填充：

df['prince'].fillna(df['prince'].mean())

3、清楚city字段的字符空格：

df['city']=df['city'].map(str.strip)

4、大小写转换：

df['city']=df['city'].str.lower()

5、更改数据格式：

df['price'].astype('int')

6、更改列名称：

df.rename(columns={'category': 'category-size'})

7、删除后出现的重复值：

df['city'].drop_duplicates()

8 、删除先出现的重复值：

df['city'].drop_duplicates(keep='last')

9、数据替换：

df['city'].replace('sh', 'shanghai')

四、数据预处理

df1=pd.DataFrame({"id":[1001,1002,1003,1004,1005,1006,1007,1008], 
"gender":['male','female','male','female','male','female','male','female'],
"pay":['Y','N','Y','Y','N','Y','N','Y',],
"m-point":[10,12,20,40,40,40,30,20]})

1、数据表合并

1.1 merge

df_inner=pd.merge(df,df1,how='inner')  # 匹配合并，交集
df_left=pd.merge(df,df1,how='left')        #
df_right=pd.merge(df,df1,how='right')
df_outer=pd.merge(df,df1,how='outer')  #并集

1.2 append

result = df1.append(df2)

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1.3 join

result = left.join(right, on='key')

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1.4 concat

pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
	          keys=None, levels=None, names=None, verify_integrity=False,
	          copy=True)

链接

https://blog.youkuaiyun.com/yiyele/article/details/80605909?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522164580274016780261999347%2522%252C%2522scm%2522%253A%252220140713.130102334…%2522%257D&request_id=164580274016780261999347&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2_alltop_positive~default-1-80605909.pc_search_result_cache&utm_term=pandas&spm=1018.2226.3001.4187