Pandas Date Converter
I'm having trouble parsing the dates of a file while reading it with Pandas.
I am using python (x, y) version 2.7.
The file I am trying to read has the following format:
"
SomethingSomethig,
SomethingSomethig,
SomethingSomethig,
Est,dir,Vmed,raj,Vmin,desv.padrão,date,
555,162,5.30,10.10,6.50,0.67,200901010000,
555,135,6.10,10.90,6.40,0.67,200901010010,
555,156,5.90,11.00,5.90,0.76,200901010020,
555,178,6.90,10.90,5.30,0.96,200901010030,
555,200,9.80,11.20,6.10,0.96,200901010040,
555,100,9.70,11.40,5.70,0.96,200901010050,"
Using the following line of code:
dados = read_csv(file, sep=",", skiprows=3, index_col=6,parse_dates=True)
output:
""
Int64Index: 157968 entries, 200901010000 to 201112312350
Data columns:
Est 157968 non-null values
dir 157968 non-null values
Vmed 157968 non-null values
raj 157968 non-null values
Vmin 157968 non-null values
desv.padr?o 157968 non-null values
Unnamed: 7 157968 non-null values
dtypes: float64(4), int64(2), object(1)
""
If the dates are not parsed. And I am getting an error when I try to use dates to do any calculations. I don't know how to use the converter and can really use your help.
+3
source to share
1 answer
Works great for me:
In [3]: read_csv('/home/wesm/tmp/foo.txt', skiprows=3, index_col=6, parse_dates=True)
Out[3]:
Est dir Vmed raj Vmin desv.padrão Unnamed: 7
date
2009-01-01 00:00:00 555 162 5.3 10.1 6.5 0.67 NaN
2009-01-01 00:10:00 555 135 6.1 10.9 6.4 0.67 NaN
2009-01-01 00:20:00 555 156 5.9 11.0 5.9 0.76 NaN
2009-01-01 00:30:00 555 178 6.9 10.9 5.3 0.96 NaN
2009-01-01 00:40:00 555 200 9.8 11.2 6.1 0.96 NaN
2009-01-01 00:50:00 555 100 9.7 11.4 5.7 0.96 NaN
Which version of pandas are you using? Maybe there is a problem that only represents the whole file?
+5
source to share