How do I convert data imported into Python from a csv file to a time series?… here is a solution to the problem.
How do I convert data imported into Python from a csv file to a time series?
I want to convert the data from python through the .csv file to a time series.
GDP = pd.read_csv('GDP.csv')
[87]: GDP
Out[87]:
GDP growth (%)
0 0.5
1 -5.2
2 -7.9
3 -9.1
4 -10.3
5 -8.8
6 -7.4
7 -10.1
8 -8.4
9 -8.7
10 -7.9
11 -4.1
Since the data imported through the .csv file is in DataFrame format, I first tried converting them to pd. Series:
GDP2 = pd. Series(data = GDP, index = pd.date_range(start = '01-2010', end = '01-2018', freq = 'Q'))
But here’s what I get:
GDP2
Out[90]:
2010-03-31 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2010-06-30 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2010-09-30 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2010-12-31 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2011-03-31 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2011-06-30 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2011-09-30 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2011-12-31 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2012-03-31 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2012-06-30 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2012-09-30 (G, D, P, , g, r, o, w, t, h, , (, %, ))
2012-12-31 (G, D, P, , g, r, o, w, t, h, , (, %, ))
When I try to pass the pd. The same happens when DataFrame does this:
GDP2 = pd. DataFrame(data = GDP, index = pd.date_range(start = '01-2010', end = '01-2018', freq = 'Q'))
GDP2
Out[92]:
GDP growth (%)
2010-03-31 NaN
2010-06-30 NaN
2010-09-30 NaN
2010-12-31 NaN
2011-03-31 NaN
2011-06-30 NaN
2011-09-30 NaN
2011-12-31 NaN
2012-03-31 NaN
2012-06-30 NaN
2012-09-30 NaN
Or when I try to do this by using reindex():
dates = pd.date_range(start = '01-2010', end = '01-2018', freq = 'Q')
dates
Out[100]:
DatetimeIndex(['2010-03-31', '2010-06-30', '2010-09-30', '2010-12-31',
'2011-03-31', '2011-06-30', '2011-09-30', '2011-12-31',
'2012-03-31', '2012-06-30', '2012-09-30', '2012-12-31',
'2013-03-31', '2013-06-30', '2013-09-30', '2013-12-31',
'2014-03-31', '2014-06-30', '2014-09-30', '2014-12-31',
'2015-03-31', '2015-06-30', '2015-09-30', '2015-12-31',
'2016-03-31', '2016-06-30', '2016-09-30', '2016-12-31',
'2017-03-31', '2017-06-30', '2017-09-30', '2017-12-31'],
dtype='datetime64[ns]', freq='Q-DEC')
GDP.reindex(dates)
Out[101]:
GDP growth (%)
2010-03-31 NaN
2010-06-30 NaN
2010-09-30 NaN
2010-12-31 NaN
2011-03-31 NaN
2011-06-30 NaN
2011-09-30 NaN
2011-12-31 NaN
2012-03-31 NaN
2012-06-30 NaN
2012-09-30 NaN
2012-12-31 NaN
I
definitely made some silly newbie mistakes and I would appreciate it if someone could help me. Cheers.
Solution
Use set_index
df
gdp
0 0.5
1 -5.2
2 -7.9
3 -9.1
4 -10.3
5 -8.8
6 -7.4
7 -10.1
8 -8.4
9 -8.7
10 -7.9
11 -4.1
df = df.set_index(pd.date_range(start = '01-2010', end = '01-2013',freq = 'Q'))
gdp
2010-03-31 0.5
2010-06-30 -5.2
2010-09-30 -7.9
2010-12-31 -9.1
2011-03-31 -10.3
2011-06-30 -8.8
2011-09-30 -7.4
2011-12-31 -10.1
2012-03-31 -8.4
2012-06-30 -8.7
2012-09-30 -7.9
2012-12-31 -4.1