pandas数据加载

 读取处理：

skiprows：跳过⼀定的⾏数

nrows：仅读取⼀定的⾏数

skipfooter：尾部有固定的⾏数不读取

skip_blank_lines：空⾏跳过

内容处理：

sep/delimiter：分隔符很重要，常⻅的有逗号，空格和Tab(‘t’)，也可以指定正则表达式 na_values：指定应该被当作na_values的数值

thousands：指定每千位分隔符

索引处理：

index_col：将真实的某列（列的数⺫，甚⾄列名）当作index

header：用作列名的行号

columns ：指定列名

squeeze：仅读到⼀列时，不再保存为pandas.Dataframe⽽是pandas.Series

import pandas as pd

import numpy as np

df=pd.read_csv('E:/BaiduNetdiskDownload/数据分析与Pandas/07_数据/data/user_info_train.txt',
               delimiter=',',encoding='gb18030',header=None,
            names=['user_id','sex','occupation','education','marriage_status','account_type'],
            index_col='user_id',na_values=4,skiprows=3,nrows=100)
print(df.head())
out：
        sex  occupation  education  marriage_status  account_type
user_id                                                           
6360     NaN         2.0        NaN              3.0           2.0
2583     2.0         2.0        2.0              1.0           1.0
34764    1.0         2.0        3.0              3.0           1.0
9554     1.0         2.0        NaN              2.0           2.0
6720     1.0         2.0        3.0              3.0           2.
print(df.info())
out：

Int64Index: 100 entries, 6360 to 1025
Data columns (total 5 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   sex              99 non-null     float64
 1   occupation       88 non-null     float64
 2   education        55 non-null     float64
 3   marriage_status  98 non-null     float64
 4   account_type     80 non-null     float64
dtypes: float64(5)
memory usage: 4.7 KB
None

df.to_csv('E:/BaiduNetdiskDownload/数据分析与Pandas/07_数据/data/liuyan.csv')
df.to_csv('E:/BaiduNetdiskDownload/数据分析与Pandas/07_数据/data/liuyan3.csv'
        ,na_rep=0,header=None,index=None,float_format='%.2f')

3.读取与写入Exlce文件。

import pandas as pd
import numpy as np
# from scipy import stats
file=pd.ExcelFile('c:/Users/liuyan/Desktop/DEM.xls')
# parse需要提取的工作表名称。
df=file.parse('Sheet1')
df1=df.iloc[:,:8]
df2=df.iloc[:,:2]
print(df1)
out：
        Index                Compounds  ...        NA-5        NA-6
0    MEDN0003                  Glycine  ...      4692.4      3061.7
1    MEDN0005              L-Threonine  ...    898160.0    855820.0
2    MEDN0006               L-Tyrosine  ...    490300.0    508090.0
3    MEDN0007               L-Arginine  ...  31803000.0  29369000.0
4    MEDN0009          L-Aspartic Acid  ...  14992000.0  15800000.0
..        ...                      ...  ...         ...         ...
615  MEDN1295  15-methyl palmitic acid  ...    121740.0    128540.0
616  MEDP0308       Dodecanedioic Aicd  ...     38549.0     33471.0
617  MEDP0429             Punicic Acid  ...    986520.0    858280.0
618  MEDP0585         Stearidonic Acid  ...    483200.0    492600.0
619  MEDP1458         Docosaenoic acid  ...     71967.0    123320.0

[620 rows x 8 columns]
print(df2)
out：
       Index                Compounds
0    MEDN0003                  Glycine
1    MEDN0005              L-Threonine
2    MEDN0006               L-Tyrosine
3    MEDN0007               L-Arginine
4    MEDN0009          L-Aspartic Acid
..        ...                      ...
615  MEDN1295  15-methyl palmitic acid
616  MEDP0308       Dodecanedioic Aicd
617  MEDP0429             Punicic Acid
618  MEDP0585         Stearidonic Acid
619  MEDP1458         Docosaenoic acid

[620 rows x 2 columns]

df1.to_excel('c:/Users/liuyan/Desktop/test.xls')