Let’s say I have 2 dataframes, both have different lengths but the same amount of columns
df1 = pd.DataFrame({'country': ['Russia','Mexico','USA','Argentina','Denmark','Syngapore'],
'population': [41,12,26,64,123,24]})
df2 = pd.DataFrame({'country': ['Russia','Argentina','Australia','USA'],
'population': [44,12,23,64]})
Lets assume that some of the data in df1 is outdated and I’ve received a new dataframe that contains some new data but not which may or may not exist already in the outdated dataframe.
I want to find out if any of the values of df2.country are inside df1.country
By doing the following I’m able to return a boolean:
df = df1.country.isin(df2.country) print(df)
Unfortunately I’m just creating a new dataframe containing the answer to my question
0 True 1 False 2 True 3 True 4 False 5 False Name: country, dtype: bool
My goal here is to delete the rows of df1 which values match with df2 and add the new data, kind of like an update.
I’ve manage to come up with something like this:
df = df1.country.isin(df2.country)
i = 0
for x in df:
if x:
df1.drop(i, inplace=True)
i += 1
frames = [df1, df2]
df1 = pd.concat(frames)
df1.reset_index(drop=True, inplace=True)
print(df1)
which in fact works and updates the dataframe
country population 0 Mexico 12 1 Denmark 123 2 Syngapore 24 3 Russia 44 4 Argentina 12 5 Australia 23 6 USA 64
But I really believe there’s a batter way of doing the same thing quicker and much more practical considering that the real dataframe is much bigger and updates every few seconds.
I’d love to hear some suggestions, Thanks!
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Answer
The isin approach is so close! Simply use the results from isin as a mask, then concat the rows from df1 that are not in (~) df2 with the rest of df2:
m = df1['country'].isin(df2['country']) df3 = pd.concat((df1[~m], df2), ignore_index=True)
df3:
country population 0 Mexico 12 1 Denmark 123 2 Syngapore 24 3 Russia 44 4 Argentina 12 5 Australia 23 6 USA 64