I have two dataframes. One has a 5 minute granularity (df1), the other is indexed by days (df2). For the sake of this example the days end at 7:10
df1:
Date Close
2019-06-20 07:00:00 2927.25
2019-06-20 07:05:00 2927.00
2019-06-20 07:10:00 2926.75
2019-06-21 07:00:00 2932.25
2019-06-21 07:05:00 2932.25
2019-06-21 07:10:00 2931.00
2019-06-24 07:00:00 2941.75
2019-06-24 07:05:00 2942.25
2019-06-24 07:10:00 2941.50
2019-06-25 07:00:00 2925.50
2019-06-25 07:05:00 2926.50
2019-06-25 07:10:00 2926.50
df2:
range
Date
2019-06-20 115.0
2019-06-21 86.0
2019-06-24 52.0
2019-06-25 132.0
Now I'd like to take the values from 'range' column of df2 and and inject them repetitive in a new column in df1.
It should look like this:
Date Close range
2019-06-20 07:00:00 2927.25 115.0
2019-06-20 07:05:00 2927.00 115.0
2019-06-20 07:10:00 2926.75 115.0
2019-06-21 07:00:00 2932.25 86.0
2019-06-21 07:05:00 2932.25 86.0
2019-06-21 07:10:00 2931.00 86.0
2019-06-24 07:00:00 2941.75 52.0
2019-06-24 07:05:00 2942.25 52.0
2019-06-24 07:10:00 2941.50 52.0
2019-06-25 07:00:00 2925.50 132.0
2019-06-25 07:05:00 2926.50 132.0
2019-06-25 07:10:00 2926.50 132.0
Unfortunately I don't know how to start that's why there's no 'my attempt' code How would you do this?
First convert the date like columns to pandas datetime series:
df1['Date'] = pd.to_datetime(df1['Date'])
df2.index = pd.to_datetime(df2.index)
Use Series.dt.date
+ Series.map
to map range
values from df2
to df1
:
df1['range'] = df1['Date'].dt.date.map(df2.set_index(df2.index.date)['range'])
OR its also possible to use DataFrame.merge
:
df1.assign(k=df1['Date'].dt.date).merge(df2.assign(k=df2.index.date), on='k').drop('k', 1)
Result:
Date Close range
0 2019-06-20 07:00:00 2927.25 115.0
1 2019-06-20 07:05:00 2927.00 115.0
2 2019-06-20 07:10:00 2926.75 115.0
3 2019-06-21 07:00:00 2932.25 86.0
4 2019-06-21 07:05:00 2932.25 86.0
5 2019-06-21 07:10:00 2931.00 86.0
6 2019-06-24 07:00:00 2941.75 52.0
7 2019-06-24 07:05:00 2942.25 52.0
8 2019-06-24 07:10:00 2941.50 52.0
9 2019-06-25 07:00:00 2925.50 132.0
10 2019-06-25 07:05:00 2926.50 132.0
11 2019-06-25 07:10:00 2926.50 132.0
If you want to make a loop, do something like that:
for i in df2["Date"]:
for j in df1["Date"]:
if i==j:
df1['range'] = df2['range']
df1 = pd.DataFrame({"Date":["2019-06-19T23:00:00.000Z","2019-06-19T23:05:00.000Z","2019-06-19T23:10:00.000Z","2019-06-20T23:00:00.000Z","2019-06-20T23:05:00.000Z","2019-06-20T23:10:00.000Z","2019-06-23T23:00:00.000Z","2019-06-23T23:05:00.000Z","2019-06-23T23:10:00.000Z","2019-06-24T23:00:00.000Z","2019-06-24T23:05:00.000Z","2019-06-24T23:10:00.000Z"],"Close":[2927.25,2927,2926.75,2932.25,2932.25,2931,2941.75,2942.25,2941.5,2925.5,2926.5,2926.5]})
df2 = pd.DataFrame({"Date":["2019-06-19T16:00:00.000Z","2019-06-20T16:00:00.000Z","2019-06-23T16:00:00.000Z","2019-06-24T16:00:00.000Z"],"range":[115,86,52,132]})
df1.Date = pd.to_datetime(df1.Date)
df2.Date = pd.to_datetime(df2.Date)
df1.assign(day=df1.Date.dt.floor("D"))\
.merge(df2.assign(day=df2.Date.dt.floor("D")), on="day")\
.drop(["day","Date_y"],1).rename({"Date_x":"Date"},axis=1)
output
Date Close range
2019-06-19 23:00:00+00:00 2927.25 115
2019-06-19 23:05:00+00:00 2927.00 115
2019-06-19 23:10:00+00:00 2926.75 115
2019-06-20 23:00:00+00:00 2932.25 86
2019-06-20 23:05:00+00:00 2932.25 86
2019-06-20 23:10:00+00:00 2931.00 86
2019-06-23 23:00:00+00:00 2941.75 52
2019-06-23 23:05:00+00:00 2942.25 52
2019-06-23 23:10:00+00:00 2941.50 52
2019-06-24 23:00:00+00:00 2925.50 132
2019-06-24 23:05:00+00:00 2926.50 132
2019-06-24 23:10:00+00:00 2926.50 132
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