# -*- coding: utf-8 -*-
import pandas as pd
from pandas import Series,DataFrame
import numpy as np
'''
读写文本格式的数据
read_cvs
read_table
'''
data=DataFrame(np.arange(16).reshape(4,4),index=list('abcd'),columns=['hhb','zjx','hcy','zjy'])
print(data)
data.to_csv('data/ex1.cvs')
rdata=pd.read_csv('data/ex1.cvs')
print(rdata)
# Unnamed: 0 hhb zjx hcy zjy
# 0 a 0 1 2 3
# 1 b 4 5 6 7
# 2 c 8 9 10 11
# 3 d 12 13 14 15
rdata=pd.read_table('data/ex1.cvs',sep=',')
print(rdata)
# Unnamed: 0 hhb zjx hcy zjy
# 0 a 0 1 2 3
# 1 b 4 5 6 7
# 2 c 8 9 10 11
# 3 d 12 13 14 15
'''
配置默认名字
'''
# data=DataFrame(np.random.randn(3,4))
# print(data)
# # 0 1 2 3
# # 0 1.095597 0.454671 0.503149 -0.337012
# # 1 -0.688659 -1.455076 0.826556 0.823949
# # 2 1.122201 0.303618 -0.399119 0.979075
#
# data.to_csv('data/ex2.cvs')
rdata=pd.read_csv('data/ex2.cvs',header=None)
print(rdata)
# 0 1 2 3
# 0 -0.310421 -0.323209 0.996199 0.927549
# 1 -0.076534 -0.160730 -1.780651 -1.069414
# 2 -0.703372 -1.265776 -0.117108 -0.164619
#配置名字
rdata=pd.read_csv('data/ex2.cvs',names=['a','b','c','d'])
print(rdata)
# a b c d
# 0 -0.310421 -0.323209 0.996199 aaa
# 1 -0.076534 -0.160730 -1.780651 bbb
# 2 -0.703372 -1.265776 -0.117108 ccc
#让d列成为行索引
names=['a','b','c','行索引']
rdata=pd.read_csv('data/ex2.cvs',names=names,index_col='行索引')
print(rdata)
# a b c
# 行索引
# aaa -0.310421 -0.323209 0.996199
# bbb -0.076534 -0.160730 -1.780651
# ccc -0.703372 -1.265776 -0.117108
'''
解读程序化
'''
# data=DataFrame(np.arange(16).reshape(8,2),index=[['one','one','one','one','two','two','two','one'],['a','b','c','d','a','b','c','d']],columns=['key1','key2'])
#
# data.to_csv('data/ex3.cvs')
# value1,value2,key1,key2
# one,a,0,1
# one,b,2,3
# one,c,4,5
# one,d,6,7
# two,a,8,9
# two,b,10,11
# two,c,12,13
# one,d,14,15
rdata=pd.read_csv('data/ex3.cvs',index_col=['value1','value2'])
print(rdata)
# key1 key2
# value1 value2
# one a 0 1
# b 2 3
# c 4 5
# d 6 7
# two a 8 9
# b 10 11
# c 12 13
# one d 14 15
#筛选阅读
rdata=pd.read_csv('data/ex4.cvs',skiprows=[0,2])
print(rdata)
# value1 value2 key1 key2
# 0 one a 0 1
# 1 one b 2 3
# 2 one c 4 5
# 3 one d 6 7
# 4 two a 8 9
# 5 two b 10 11
# 6 two c 12 13
# 7 one d 14 15
data=pd.read_csv('data/ex5.cvs')
print(data)
# value1 value2 key1 key2
# 0 one NaN 0 1
# 1 one b 2 3
# 2 one c 4 Na
#指定NaN值
sentinels={'key2':[3,'Na'],'value2':'b'}
data=pd.read_csv('data/ex5.cvs',na_values=sentinels)
print(data)
# value1 value2 key1 key2
# 0 one NaN 0 1.0
# 1 one NaN 2 NaN
# 2 one c 4 NaN
'''
逐块读取文件
'''
data=pd.read_csv('data/ex4.cvs',nrows=2,skiprows=[0,2])
print(data)
# value1 value2 key1 key2
# 0 one a 0 1
# 1 one b 2 3
#将数据分成块
chunker=pd.read_csv('data/ex3.cvs',chunksize=2)
print(chunker)#<pandas.io.parsers.TextFileReader object at 0x000000000B1B9F60>
tot=Series([])
for piece in chunker:
tot=tot.add(piece['value1'].value_counts(),fill_value=0)
print(tot)
# one 5.0
# two 3.0
# dtype: float64
print(tot[0])
'''
输出文本格式，自定义分隔符
'''
data=pd.read_csv('data/ex3.cvs')
print(data)
import sys
data.to_csv('data/ex6.cvs',sep='|')
# |value1|value2|key1|key2
# 0|one|a|0|1
# 1|one|b|2|3
# 2|one|c|4|5
# 3|one|d|6|7
# 4|two|a|8|9
# 5|two|b|10|11
# 6|two|c|12|13
# 7|one|d|14|15
data=pd.read_csv('data/ex5.cvs')
data.to_csv('data/ex7.cvs',na_rep='null')#将空字符串定义为null
# ,value1,value2,key1,key2
# 0,one,null,0,1
# 1,one,b,2,3
# 2,one,c,4,Na
#禁用索引
data.to_csv('data/ex8.cvs',index=False,header=False)
# one,,0,1
# one,b,2,3
# one,c,4,Na
#指定排列顺序
data.to_csv('data/ex9.cvs',columns=['key1','key2','value1','value2'])
# ,key1,key2,value1,value2
# 0,0,1,one,
# 1,2,3,one,b
# 2,4,Na,one,c
'''
json
'''
import json
obj='{"name":"hb","data":[1,2,3,4,5],"dict":[{"name":"aa"},{"name":"bb"}],"Na":1}'
# result=json.dumps(obj)
# print(result)
data=json.loads(obj)
print(data)
dict=DataFrame(data['dict'],columns=['name'])
print(dict)
# name
# 0 aa
# 1 bb
python数据分析：pandas的读取和写入文件
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