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numpy_test.py
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numpy_test.py
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#!/home/zkfr/.local/share/virtualenvs/xf-5EfV3Nly/bin/python
#-*- coding:utf-8 -*-
# @author : MaLei
# @datetime : 2018-10-19 13:46
# @file : numpy_test.py
# @software : PyCharm
import numpy as np
# a=np.arange(24)
# print(a.ndim)
# print(a.shape)
# print(a.itemsize)
# a = np.arange(0,60,5)
# for x in np.nditer(a):
# print(x)
# a = np.arange(0,60,5)
# a = a.reshape(3,4)
# b = a.T
# c = b.copy(order='C') #以 C 风格顺序排序
# for x in np.nditer(c):
# print(x)
# c = b.copy(order='F') #以 F 风格顺序排序
# for x in np.nditer(c):
# print(x)
import pandas as pd
# 查看每一列的数据类型
# names = ['Bob','Jessica','Mary','John','Mel','John','Mel']
# births = [968, 155, 77, 578, 973, 578, 973]
# data=list(zip(names,births))
# df=pd.DataFrame(data=data,columns=['names','births'])
# # 查看每一列的数据类型
# print df.dtypes
# # 查看指定列的数据类型
# print df
# d = {'one':[1,1,1,1,1],
# 'two':[2,2,2,2,2],
# 'letter':['a','a','b','b','c']}
#
# # 创建一个 dataframe
# df = pd.DataFrame(d)
# print df
#
# one=df.groupby('letter')
# two=df.groupby(['letter','one']).sum()
# # 不把用来分组的列名作为索引
# three=df.groupby(['letter','one'],as_index=False).sum()
# print one.sum()
# print two
# # 输出索引
# print two.index
# print three.index
# 创建一个 dataframe，用日期作为索引
# States = ['NY', 'NY', 'NY', 'NY', 'FL', 'FL', 'GA', 'GA', 'FL', 'FL']
# # data = [1.0, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# # idx = pd.date_range('1/1/2012', periods=10, freq='MS')
# # df1 = pd.DataFrame(data, index=idx, columns=['Revenue'])
# # df1['State'] = States
# #
# # # 创建第二个 dataframe
# # data2 = [10.0, 10.0, 9, 9, 8, 8, 7, 7, 6, 6]
# # idx2 = pd.date_range('1/1/2013', periods=10, freq='MS')
# # df2 = pd.DataFrame(data2, index=idx2, columns=['Revenue'])
# # df2['State'] = States
# #
# # # 把两个 dataframe 合并起来
# # df = pd.concat([df1,df2])
# # print df
import pymysql
import sys,os
reload(sys)
sys.setdefaultencoding('utf-8')
# sql='SELECT NEWSTITLE,NEWSTYPE FROM WEBNEWS'
# conn=pymysql.connect(host='localhost',
# port=3306,
# user='root',
# passwd='zkyr1006',
# db='news',
# charset='utf8')
# result=pd.read_sql(sql,conn)
# conn.close()
# # 导出为csv格式
# result.to_csv(r'./news.csv',index=False)
# # 导出为txt格式
# result.to_csv(r'./news.txt',index=False)
# # 导出为Excel
# result.to_excel(r'./news.xls',index=False,sheet_name='news')
# # 导出为json
# result.to_json(r'./news.json')
# data=pd.read_excel('news.xls',sheet_name='news',index_col='NEWSTYPE')
# print data
# data=pd.read_json('news.json')
# print data
# df1=pd.DataFrame({'a':[1],'b':['ceshi']})
# print df1
# df1.to_excel('test1.xls',index=False)
# df1.to_excel('test2.xls',index=False)
# df1.to_excel('test3.xls',index=False)
# 定义读取数据的函数
def get_file(locate):
loc=r'./'+locate
# print loc
df=pd.read_excel(loc,0)
df['file']=locate
df.set_index('file')
return df
# 存储所有Excel表的名字
filenames=[]
# 你存放Excel文件的路径可能不一样，需要修改。
os.chdir(r"./")
for files in os.listdir('.'):
if files.endswith('.xls'):
filenames.append(files)
# 创建一个 dataframe 的 list
df_list=[get_file(fname) for fname in filenames]
# 把 list 中所有的 dataframe 合并成一个
big_df=pd.concat(df_list)