Python利用pandas庫對文件的處理

一.數據導入和導出 （一）讀取csv文件  1.本地讀取

import pandas as pd
df = pd.read_csv('E:\\tips.csv')  #根據自己數據文件保存的路徑填寫(p.s.  python填寫路徑時，要么使用/，要么使用\\)
#輸出：
     total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
5         25.29  4.71    Male     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2
[244 rows x 7 columns]

2.網絡讀取

import pandas as pd
data_url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv" #填寫url讀取
df = pd.read_csv(data_url)
#輸出同上，為了節省篇幅這兒就不粘貼了

3.read_csv詳解  功能： Read CSV (comma-separated) file into DataFrame

read_csv(filepath_or_buffer, sep=',', dialect=None, compression='infer', doublequote=True, escapechar=None, quotechar='"', quoting=0, skipinitialspace=False, lineterminator=None, header='infer', index_col=None, names=None, prefix=None, skiprows=None, skipfooter=None, skip_footer=0, na_values=None, true_values=None, false_values=None, delimiter=None, converters=None, dtype=None, usecols=None, engine=None, delim_whitespace=False, as_recarray=False, na_filter=True, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, warn_bad_lines=True, error_bad_lines=True, keep_default_na=True, thousands=None, comment=None, decimal='.', parse_dates=False, keep_date_col=False, dayfirst=False, date_parser=None, memory_map=False, float_precision=None, nrows=None, iterator=False, chunksize=None, verbose=False, encoding=None, squeeze=False, mangle_dupe_cols=True, tupleize_cols=False, infer_datetime_format=False, skip_blank_lines=True)

參數詳解： http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html  (二)讀取Mysql數據 　　假設數據庫安裝在本地，用戶名為myusername,密碼為mypassword,要讀取mydb數據庫中的數據

import pandas as pd
import MySQLdb
mysql_cn= MySQLdb.connect(host='localhost', port=3306,user='myusername', passwd='mypassword', db='mydb')
df = pd.read_sql('select * from test;', con=mysql_cn)    
mysql_cn.close()

上面的代碼讀取了test表中所有的數據到df中，而df的數據結構為Dataframe。
ps.MySQL教程:http://www.runoob.com/mysql/mysql-tutorial.html
(三)讀取excel文件
要讀取excel文件還需要安裝xlrd模塊，pip install xlrd即可。

df = pd.read_excel('E:\\tips.xls')

(四)數據導出到csv文件

df.to_csv('E:\\demo.csv', encoding='utf-8', index=False) 
#index=False表示導出時去掉行名稱，如果數據中含有中文，一般encoding指定為‘utf-8’

 

 (五)讀寫SQL數據庫

import pandas as pd
import sqlite3
con = sqlite3.connect('...')
sql = '...'
df=pd.read_sql(sql,con)

#help文件
help(sqlite3.connect)
#輸出
Help on built-in function connect in module _sqlite3:

connect(...)
    connect(database[, timeout, isolation_level, detect_types, factory])
    
    Opens a connection to the SQLite database file *database*. You can use
    ":memory:" to open a database connection to a database that resides in
    RAM instead of on disk.
#############
help(pd.read_sql)
#輸出
Help on function read_sql in module pandas.io.sql:

read_sql(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None)
    Read SQL query or database table into a DataFrame.

轉載鏈接：https://www.cnblogs.com/zzhzhao/p/5269217.html#undefined