[英]python read date and time from csv
my data looks like that: 我的数据如下所示:
GIdx,Date,num,Time
1,11/28/2012,20,10:05:50
1,11/28/2012,20,10:05:50
2,11/28/2012,20,10:09:24
2,11/28/2012,20,10:09:24
2,11/28/2012,20,10:09:25
2,11/28/2012,20,10:09:25
2,11/28/2012,20,10:09:26
3,11/28/2012,20,10:09:34
3,11/28/2012,20,10:09:34
i try to read column Date as datetime
and column Time as time
but when I check the their type I get Series
: 我尝试将Date列读取为datetime
并将Time列读取为time
但是当我检查其类型时,我会得到Series
:
type(df['Date'])
class pandas.core.series.Series
type(df_original['Time'])
class pandas.core.series.Series
I did something like: 我做了类似的事情:
df=pd.read_csv(filename,sep=",", header = 0, na_values=['NA'])
You can add to read_csv
parameter parse_dates
with columns where are dates
and times
: 您可以将带有dates
和times
列添加到read_csv
参数parse_dates
:
import pandas as pd
import io
temp=u"""GIdx,Date,num,Time
1,11/28/2012,20,10:05:50
1,11/28/2012,20,10:05:50
2,11/28/2012,20,10:09:24
2,11/28/2012,20,10:09:24
2,11/28/2012,20,10:09:25
2,11/28/2012,20,10:09:25
2,11/28/2012,20,10:09:26
3,11/28/2012,20,10:09:34
3,11/28/2012,20,10:09:34"""
#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), parse_dates=[['Date','Time']])
print (df)
Date_Time GIdx num
0 2012-11-28 10:05:50 1 20
1 2012-11-28 10:05:50 1 20
2 2012-11-28 10:09:24 2 20
3 2012-11-28 10:09:24 2 20
4 2012-11-28 10:09:25 2 20
5 2012-11-28 10:09:25 2 20
6 2012-11-28 10:09:26 2 20
7 2012-11-28 10:09:34 3 20
8 2012-11-28 10:09:34 3 20
print (df.dtypes)
Date_Time datetime64[ns]
GIdx int64
num int64
dtype: object
You can omit parameters sep=","
, header = 0
and na_values=['NA']
, because there are by default: 您可以省略参数sep=","
, header = 0
和na_values=['NA']
,因为默认情况下有:
df=pd.read_csv(filename,sep=",", header = 0, na_values=['NA'])
df=pd.read_csv(filename)
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