Pandas基本用法（一）：创建与查询

一、Pandas创建

1、导入库：

import numpy as np

import pandas as pd

2、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'])

df

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3、数据表信息查看

维度查看：

df.shape

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(6, 6)

数据表基本信息:

df.info()

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<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 6 columns):
id 6 non-null int64
date 6 non-null datetime64[ns]
city 6 non-null object
category 6 non-null object
age 6 non-null int64
price 4 non-null float64
dtypes: datetime64[ns](1), float64(1), int64(2), object(2)
memory usage: 368.0+ bytes

数据的格式：

df.dtypes
----------------------------
id int64
date datetime64[ns]
city object
category object
age int64
price float64
dtype: object

查看某一列的唯一值：

df['city'].unique()

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array(['Beijing ', 'SH', ' guangzhou ', 'Shenzhen', 'shanghai', 'BEIJING '], dtype=object)

查看列名称：

df.columns

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Index(['id', 'date', 'city', 'category', 'age', 'price'], dtype='object')

查看前几行和后几行数据：

df.head(2)

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df.tail(2)

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BY：HW-CJ