は、必要なパラメータnames
と[1]
とparse_dates
ある - datetime
に2つ目の列を解析してみてください:
import pandas as pd
import numpy as np
from pandas.compat import StringIO
temp=u"""156,2014-02-01 00:00:00.739166+01,41.8836718276551,12.4877775603346
187,1014-02-01 00:00:01.148457+01,41.9285433333333,12.4690366666667
297,2014-02-01 00:00:01.220066+01,41.8910686119733,12.4927045625339
89,2014-02-01 00:00:01.470854+01,41.7931766914244,12.4321219603157
79,2014-02-01 00:00:01.631136+01,41.90027472,12.46274618
191,2014-02-01 00:00:02.048546+01,41.8523047579646,12.5774065771898
343,2014-02-01 00:00:02.647839+01,41.8921718255185,12.4696996165151
341,2014-02-01 00:00:02.709888+01,41.9102125627332,12.4770004336041
260,2014-02-01 00:00:03.458195+01,41.8658208551143,12.4655221109313"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp),
parse_dates=[1],
names=['userid','datetime','latitude','longitude'])
#print (df)
#check dtypes if datetime it is OK
print (df['datetime'].dtypes)
datetime64[ns]
df['datetime'] = df['datetime'].astype(np.int64) // 10**9
print (df)
userid datetime latitude longitude
0 156 1391209200 41.883672 12.487778
1 187 1391209201 41.928543 12.469037
2 297 1391209201 41.891069 12.492705
3 89 1391209201 41.793177 12.432122
4 79 1391209201 41.900275 12.462746
5 191 1391209202 41.852305 12.577407
6 343 1391209202 41.892172 12.469700
7 341 1391209202 41.910213 12.477000
8 260 1391209203 41.865821 12.465522
別の可能性のある問題は、私のサンプル2行目、悪いデータである:
import pandas as pd
from pandas.compat import StringIO
temp=u"""156,2014-02-01 00:00:00.739166+01,41.8836718276551,12.4877775603346
187,1014-02-01 00:00:01.148457+01,41.9285433333333,12.4690366666667
297,2014-02-01 00:00:01.220066+01,41.8910686119733,12.4927045625339
89,2014-02-01 00:00:01.470854+01,41.7931766914244,12.4321219603157
79,2014-02-01 00:00:01.631136+01,41.90027472,12.46274618
191,2014-02-01 00:00:02.048546+01,41.8523047579646,12.5774065771898
343,2014-02-01 00:00:02.647839+01,41.8921718255185,12.4696996165151
341,2014-02-01 00:00:02.709888+01,41.9102125627332,12.4770004336041
260,2014-02-01 00:00:03.458195+01,41.8658208551143,12.4655221109313"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp),
parse_dates=[1],
names=['userid','datetime','latitude','longitude'])
#print (df)
#check dtypes - parse failed, get object dtype
print (df['datetime'].dtypes)
object
解析to_datetime
とパラメータerrors='coerce'
でdatetime型にする - それはNaT
に不良データを交換し、その後、いくつかのNATを置き換えます値fillna
と0
(1970-01-01 00:00:00.000000
):
df['datetime'] = pd.to_datetime(df['datetime'], errors='coerce').fillna(0)
print (df)
userid datetime latitude longitude
0 156 2014-01-31 23:00:00.739166 41.883672 12.487778
1 187 1970-01-01 00:00:00.000000 41.928543 12.469037
2 297 2014-01-31 23:00:01.220066 41.891069 12.492705
3 89 2014-01-31 23:00:01.470854 41.793177 12.432122
4 79 2014-01-31 23:00:01.631136 41.900275 12.462746
5 191 2014-01-31 23:00:02.048546 41.852305 12.577407
6 343 2014-01-31 23:00:02.647839 41.892172 12.469700
7 341 2014-01-31 23:00:02.709888 41.910213 12.477000
8 260 2014-01-31 23:00:03.458195 41.865821 12.465522
df['datetime'] = df['datetime'].astype(np.int64) // 10**9
print (df)
userid datetime latitude longitude
0 156 1391209200 41.883672 12.487778
1 187 0 41.928543 12.469037
2 297 1391209201 41.891069 12.492705
3 89 1391209201 41.793177 12.432122
4 79 1391209201 41.900275 12.462746
5 191 1391209202 41.852305 12.577407
6 343 1391209202 41.892172 12.469700
7 341 1391209202 41.910213 12.477000
8 260 1391209203 41.865821 12.465522
EDIT:
がある場合も、ヘッダーと必要header=0
がread_csv
に追加する列名を置き換える必要があります。
ありがとうございます。次のエラーが表示されます。 'ValueError:' datetime 'がリストにありません。csvファイルの同じ入力がありません。 –
CSVファイルの最初の3行はここに貼り付けられますか? –
私はそこからダウンロードできるリンクとして入力ファイルを与えました –