Reputation: 65600
I have two dataframes, both indexed by a date column called month
. The first, df1
, has eight rows. The column I care about is df['num_percent']
and it looks like this:
2015-02-01 0.071549
2015-03-01 0.070368
2015-04-01 0.069291
2015-05-01 0.068394
2015-06-01 0.067452
2015-07-01 0.066302
2015-08-01 0.065543
2015-09-01 0.064591
Name: num_percent, dtype: float64
The second dataframe has 100,000 rows. The column I care about is df2['total_quantity']
and a sample of it looks like this:
2014-11-01 324199
2014-12-01 378443
2015-01-01 367379
2015-02-01 336863
2015-03-01 380268
2015-04-01 386292
2015-05-01 373213
2015-06-01 403343
2015-07-01 414310
2015-08-01 403684
2015-09-01 420922
Name: total_quantity, dtype: int64
I want to add a new column to df2
which is the value of df2['total_quantity']
multiplied by the corresponding value for the month in df1
.
How can I do this?
If I try:
df2['percent'] = df2['total_quantity'] * df1['num_percent']
I get ValueError: cannot reindex from a duplicate axis
.
UPDATE: Here's some data and code to replicate the problem:
data = {'month': ['2014-01-01', '2014-02-01', '2014-03-01'],
'num_percent': [0.4, 0.5, 0.6]}
df1 = pd.DataFrame(data)
df1['month'] = pd.to_datetime(df1['month'])
df1 = df1.set_index('month')
data = {'month': ['2014-01-01', '2014-02-01', '2014-03-01', '2014-01-01'],
'org': ['00K', '00K', '00K', '00L'],
'total_quantity': [1000, 1000, 2000, 1000]}
df2 = pd.DataFrame(data)
df2['month'] = pd.to_datetime(df2['month'])
df2 = df2.set_index('month')
# Both of these produce ValueError: cannot reindex...
df2['percent'] = df1['num_percent'] * df2['total_quantity']
df2.loc[df2.index.isin(df1.index), 'percent'] = df2['total_quantity'] * df1['num_percent']
Upvotes: 6
Views: 4597
Reputation: 394459
If you join
the dfs first then you can then multiply:
In [24]:
df3 = df1.join(df2)
df3['percent'] = df3['num_percent'] * df3['total_quantity']
df3
Out[24]:
num_percent org total_quantity percent
month
2014-01-01 0.4 00K 1000 400
2014-01-01 0.4 00L 1000 400
2014-02-01 0.5 00K 1000 500
2014-03-01 0.6 00K 2000 1200
Upvotes: 5