原文地址：
https://github.com/AsuraDong/Blog/blob/master/Articles/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E5%AD%98%E5%82%A8%E5%92%8C%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md
1.读取文本格式数据
import pandas as pd
import numpy as np
import sys
import pymysql
df = pd.read_csv('ex1.csv')
print(df)
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo
df = pd.read_table('ex1.csv',sep=',') #可以使用read_table，但必须指定分隔符
# sep还可以是正则表达式
print(df)
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo
df = pd.read_csv('ex2.csv',header = None)#不是每一个csv都有header
print(df)
0 1 2 3 4
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo
df = pd.read_csv('ex2.csv',names=['a','b','c','d','names'])#指定名字
print(df)
a b c d names
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo
names=['a','b','c','d','names']
df = pd.read_csv('ex2.csv',names=names,index_col='names') #将names做成索引
print(df)
#names对应三个，abcd分别有对应的
a b c d
names
hello 1 2 3 4
world 5 6 7 8
foo 9 10 11 12
df = pd.read_csv('csv_mindex.csv')
print('原始样子：','\n',df)
df = pd.read_csv('csv_mindex.csv',index_col=['keys','key2'])
#层次化索引.
#请注意keys和key2的顺序
print(df)
原始样子：
keys key2 value1 value2
0 one a 1 2
1 one b 3 4
2 two a 9 10
3 two c 13 14
value1 value2
keys key2
one a 1 2
b 3 4
two a 9 10
c 13 14
df = pd.read_csv('ex4.csv')
print('原始样子：','\n',df)
#跳过文件的第几行
print()
df = pd.read_csv('ex4.csv',skiprows=[0,2])
print(df)
原始样子：
# hey!
a b c d message
# just wanted to make things more difficult NaN NaN NaN NaN
1 2 NaN 4 hello
a b c d message
0 1 2 NaN 4 hello
pd.isnull(df)# 处理缺失值
df = pd.read_csv('ex4.csv',skiprows=[0,2],na_values=['hello'])# 接收一组用于表示缺失值的字符串
print(df)
print(pd.isnull(df))
a b c d message
0 1 2 NaN 4 NaN
a b c d message
0 False False True False True
sentinels = {'message':['foo','NA'],'d':['a','NaN']}# 用一个字典为各列指定不同的NA标记值
df = pd.read_csv('ex4.csv',skiprows=[0,2],na_values=sentinels)
print(df)
a b c d message
0 1 2 NaN 4 hello
2.逐块读取文本文件
# nrows参数指定只读取定行。算上第一行哦
pd.read_csv('ex1.csv',nrows=4)
<style> .dataframe thead tr:only-child th { text-align: right; }
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
</style>
| | a | b | c | d | message |
|---|
| 0 |
1 |
2 |
3 |
4 |
hello |
|---|
| 1 |
5 |
6 |
7 |
8 |
world |
|---|
| 2 |
9 |
10 |
11 |
12 |
foo |
|---|
# chunksize 指定分块读取
chunks = pd.read_csv('ex1.csv',chunksize=2)
print(chunks)
<pandas.io.parsers.TextFileReader object at 0x0000007D7E4A39B0>
for chunk in chunks:
print(chunk)
print('='*10,)
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
==========
a b c d message
2 9 10 11 12 foo
==========
3.将数据写出到文本格式
data = pd.read_csv('ex1.csv',nrows=3)
data.to_csv('ex1_1.csv') #to_csv写入
data.to_csv('ex1_2.csv',sep='|')# 别的分隔符
data.to_csv('ex1_1.csv',na_rep='NULL')# 缺失值会被替换为na_rep
data.to_csv(sys.stdout,index=False,header=False)
# 行、列标签被禁止
# 输出到控制台
1,2,3,4,hello
5,6,7,8,world
9,10,11,12,foo
data.to_csv(sys.stdout,index=False,columns=['a','b'])
a,b
1,2
5,6
9,10
,a,b,c,d,message
0,1,2,3,4,hello
1,5,6,7,8,world
2,9,10,11,12,foo
4.DataFrame
# 可以将json格式的数据传给DataFreame
# 也可以数据将数据库的rows传给DataFrame
conn = pymysql.Connect(host='172.31.238.166',port=3306,user='luowang',passwd='root',\
charset='UTF8',db='dyx')
cursor=conn.cursor()
sql='select * from access_log';
cursor.execute(sql)
rows= cursor.fetchall()
print(cursor.description)
(('aid', 3, None, 16, 16, 0, False), ('site_id', 3, None, 16, 16, 0, False), ('count', 3, None, 32, 32, 0, False))
# cursor.description第一个保存了列的信息
# pd.DataFrame(rows,columns=[i[0] for i in cursor.description])
pd.DataFrame(rows,columns=zip(*cursor.description)[0])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-74-05969a36ac33> in <module>()
1 # cursor.description第一个保存了列的信息
2 # pd.DataFrame(rows,columns=[i[0] for i in cursor.description])
----> 3 pd.DataFrame(rows,columns=zip(*cursor.description)[0])
TypeError: 'zip' object is not subscriptable
[i[0] for i in cursor.description]
['aid', 'site_id', 'count']
pd.DataFrame(list(rows),columns=[i[0] for i in cursor.description]) #rows必须是list类型
<style> .dataframe thead tr:only-child th { text-align: right; }
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
</style>
| | aid | site_id | count |
|---|
| 0 |
1 |
1 |
45 |
|---|
| 1 |
2 |
3 |
100 |
|---|
| 2 |
3 |
1 |
230 |
|---|
| 3 |
4 |
2 |
10 |
|---|
| 4 |
5 |
5 |
205 |
|---|
| 5 |
6 |
4 |
13 |
|---|
| 6 |
7 |
3 |
220 |
|---|
| 7 |
8 |
5 |
545 |
|---|
| 8 |
9 |
3 |
201 |
|---|
| 9 |
10 |
10 |
10 |
|---|
| 10 |
11 |
11 |
11
|
|---|
