Pandas- 读取 csv/txt/excel/mysql 数据

1、读取纯文本文件

1.1 读取 CSV，使用默认的标题行、逗号分隔符

fpath = "./datas/ml-latest-small/ratings.csv"

# 使用pd.read_csv读取数据
ratings = pd.read_csv(fpath)

# 查看前几行数据
ratings.head()

# 查看数据的形状，返回(行数、列数)
ratings.shape

(100836, 4)

# 查看列名列表
ratings.columns

Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')

# 查看索引列
ratings.index

RangeIndex(start=0, stop=100836, step=1)

# 查看每列的数据类型
ratings.dtypes

userId         int64
movieId        int64
rating       float64
timestamp      int64
dtype: object

1.2 读取 txt 文件，自己指定分隔符、列名

fpath = "./datas/crazyant/access_pvuv.txt"

pvuv = pd.read_csv(
 fpath,
 sep="\t",
 header=None,
 names=['pdate', 'pv', 'uv']
)

pvuv

2、读取 excel 文件

fpath = "./datas/crazyant/access_pvuv.xlsx" 
pvuv = pd.read_excel(fpath)

pvuv

3、读取 MySQL 数据库

import pymysql
conn = pymysql.connect(
        host='127.0.0.1',
        user='root',
        password='12345678',
        database='test',
        charset='utf8'
    )

mysql_page = pd.read_sql("select * from crazyant_pvuv", con=conn)

mysql_page