I downloaded the Broad Dollar Index from FRED with the following format:
DATE RTWEXBGS
0 2006-01-01 100.0000
1 2006-02-01 100.2651
2 2006-03-01 100.5424
3 2006-04-01 100.0540
4 2006-05-01 97.8681
.. ... ...
194 2022-03-01 111.2659
195 2022-04-01 111.8324
196 2022-05-01 114.6075
197 2022-06-01 115.6957
198 2022-07-01 118.2674
I also got an Excel file of inflation rate with a different format:
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
0 2022 0.07480 0.07871 0.08542 0.08259 0.08582 0.09060 0.08525 NaN NaN NaN NaN NaN NaN
1 2021 0.01400 0.01676 0.02620 0.04160 0.04993 0.05391 0.05365 0.05251 0.05390 0.06222 0.06809 0.07036 0.04698
2 2020 0.02487 0.02335 0.01539 0.00329 0.00118 0.00646 0.00986 0.01310 0.01371 0.01182 0.01175 0.01362 0.01234
3 2019 0.01551 0.01520 0.01863 0.01996 0.01790 0.01648 0.01811 0.01750 0.01711 0.01764 0.02051 0.02285 0.01812
4 2018 0.02071 0.02212 0.02360 0.02463 0.02801 0.02872 0.02950 0.02699 0.02277 0.02522 0.02177 0.01910 0.02443
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ...
104 1918 0.19658 0.17500 0.16667 0.12698 0.13281 0.13077 0.17969 0.18462 0.18045 0.18519 0.20741 0.20438 0.17284
105 1917 0.12500 0.15385 0.14286 0.18868 0.19626 0.20370 0.18519 0.19266 0.19820 0.19469 0.17391 0.18103 0.17841
106 1916 0.02970 0.04000 0.06061 0.06000 0.05941 0.06931 0.06931 0.07921 0.09901 0.10784 0.11650 0.12621 0.07667
107 1915 0.01000 0.01010 0.00000 0.02041 0.02020 0.02020 0.01000 -0.00980 -0.00980 0.00990 0.00980 0.01980 0.00915
108 1914 0.02041 0.01020 0.01020 0.00000 0.02062 0.01020 0.01010 0.03030 0.02000 0.01000 0.00990 0.01000 0.01349
How do I change the inflation table into a format similar to the dollar index?
CodePudding user response:
Something like this(didn't take column=Annual
into account),
df
###
Year Jan Feb Mar Apr May Jun Jul Aug \
0 2022 0.07480 0.07871 0.08542 0.08259 0.08582 0.09060 0.08525 NaN
1 2021 0.01400 0.01676 0.02620 0.04160 0.04993 0.05391 0.05365 NaN
2 2020 0.02487 0.02335 0.01539 0.00329 0.00118 0.00646 0.00986 NaN
Sep Oct Nov Dec Annual
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
month = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
df_melt = pd.melt(df, id_vars=['Year'], value_vars=month, var_name='Month', value_name='Sales')
df_melt['Date'] = pd.to_datetime(df_melt['Year'].astype(str) '-' df_melt['Month'].astype(str))
# convert Date column to datetime type
df_melt = df_melt[['Date', 'Sales']]
df_melt
###
Date Sales
0 2022-01-01 0.07480
1 2021-01-01 0.01400
2 2020-01-01 0.02487
3 2022-02-01 0.07871
4 2021-02-01 0.01676
5 2020-02-01 0.02335
6 2022-03-01 0.08542
7 2021-03-01 0.02620
8 2020-03-01 0.01539
9 2022-04-01 0.08259
10 2021-04-01 0.04160
11 2020-04-01 0.00329
12 2022-05-01 0.08582
13 2021-05-01 0.04993
14 2020-05-01 0.00118
15 2022-06-01 0.09060
16 2021-06-01 0.05391
17 2020-06-01 0.00646
18 2022-07-01 0.08525
19 2021-07-01 0.05365
20 2020-07-01 0.00986
21 2022-08-01 NaN
22 2021-08-01 NaN
23 2020-08-01 NaN
24 2022-09-01 NaN
25 2021-09-01 NaN
26 2020-09-01 NaN
27 2022-10-01 NaN
28 2021-10-01 NaN
29 2020-10-01 NaN
30 2022-11-01 NaN
31 2021-11-01 NaN
32 2020-11-01 NaN
33 2022-12-01 NaN
34 2021-12-01 NaN
35 2020-12-01 NaN