Say I have two data frames:
df1:
A
0 a
1 b
df2:
A
0 a
1 c
I want the result to be the union of the two frames with an extra column showing the source data frame that the row belongs to. In case of duplicates, duplicates should be removed and the respective extra column should show both sources:
A B
0 a df1, df2
1 b df1
2 c df2
I can get the concatenated data frame (df3) without duplicates as follows:
import pandas as pd
df3=pd.concat([df1,df2],ignore_index=True).drop_duplicates().reset_index(drop=True)
I can't think of/find a method to have control over what element goes where. How can I add the extra column?
Thank you very much for any tips.
Merge with an indicator argument, and remap the result:
m = {'left_only': 'df1', 'right_only': 'df2', 'both': 'df1, df2'}
result = df1.merge(df2, on=['A'], how='outer', indicator='B')
result['B'] = result['B'].map(m)
result
A B
0 a df1, df2
1 b df1
2 c df2
Use the command below:
df3 = pd.concat([df1.assign(source='df1'), df2.assign(source='df2')]) \
.groupby('A') \
.aggregate(list) \
.reset_index()
The result will be:
A source
0 a [df1, df2]
1 b [df1]
2 c [df2]
The assign will add a column named source with value df1 and df2 to your dataframes. groupby command groups rows with same A value to single row. aggregate command describes how to aggregate other columns (source) for each group of rows with same A. I have used list aggregate function so that the source column be the list of values with same A.
We use outer join to solve this -
df1 = pd.DataFrame({'A':['a','b']})
df2 = pd.DataFrame({'A':['a','c']})
df1['col1']='df1'
df2['col2']='df2'
df=pd.merge(df1, df2, on=['A'], how="outer").fillna('')
df['B']=df['col1']+','+df['col2']
df['B'] = df['B'].str.strip(',')
df=df[['A','B']]
df
A B
0 a df1,df2
1 b df1
2 c df2
Related
I have two dataframes:
df1 with columns A and B and df2 with columns C and D.
I want to merge df1 and df2 under the condition that A contains D.
df1 = [['Hello world',10],['bla bla',12],['okey',13]]
df2 = [['rld',100]]
Result
df_1_2 = [['rld',100,10]]
So the result contains all columns of df1 and df2 (except column A of df1).
We can do findall find the substring then merge
df1['new'] = df1[0].str.findall('|'.join(df2[0].tolist())).str[0]
out = df1.merge(df2,left_on='new',right_on=0)
0_x 1_x new 0_y 1_y
0 Hello world 10 rld rld 100
I have two data frames with the same columns, and similar content.
I'd like apply the same functions on each, without having to brute force them, or concatenate the dfs. I tried to pass the objects into nested dictionaries, but that seems more trouble than it's worth (I don't believe dataframe.to_dict supports passing into an existing list).
However, it appears that the for loop stores the list of dfs in the df object, and I don't know how to get it back to the original dfs... see my example below.
df1 = {'Column1': [1,2,2,4,5],
'Column2': ["A","B","B","D","E"]}
df1 = pd.DataFrame(df1, columns=['Column1','Column2'])
df2 = {'Column1': [2,11,2,2,14],
'Column2': ["B","Y","B","B","V"]}
df2 = pd.DataFrame(df2, columns=['Column1','Column2'])
def filter_fun(df1, df2):
for df in (df1, df2):
df = df[(df['Column1']==2) & (df['Column2'].isin(['B']))]
return df1, df2
filter_fun(df1, df2)
If you write the filter as a function you can apply it in a list comprehension:
def filter(df):
return df[(df['Column1']==2) & (df['Column2'].isin(['B']))]
df1, df2 = [filter(df) for df in (df1, df2)]
I would recommend concatenation with custom specified keys, because 1) it is easy to assign it back, and 2) you can do the same operation once instead of twice.
# Concatenate df1 and df2
df = pd.concat([df1, df2], keys=['a', 'b'])
# Perform your operation
out = df[(df['Column1'] == 2) & df['Column2'].isin(['B'])]
out.loc['a'] # result for `df1`
Column1 Column2
1 2 B
2 2 B
out.loc['b'] # result for `df2`
Column1 Column2
0 2 B
2 2 B
3 2 B
This should work fine for most operations. For groupby, you will want to group on the 0th index level as well.
I have two dataframes with a common column called 'upc' as such:
df1:
upc
23456793749
78907809834
35894796324
67382808404
93743008374
df2:
upc
4567937
9078098
8947963
3828084
7430083
Notice that df2 'upc' values are the innermost 7 values of df1 'upc' values.
Note that both df1 and df2 have other columns not shown above.
What I want to do is do an inner merge on 'upc' but only on the innermost 7 values. How can I achieve this?
1) Create both dataframes and convert to string type.
2) pd.merge the two frames, but using the left_on keyword to access the inner 7 characters of your 'upc' series
df1 = pd.DataFrame(data=[
23456793749,
78907809834,
35894796324,
67382808404,
93743008374,], columns = ['upc1'])
df1 = df1.astype(str)
df2 = pd.DataFrame(data=[
4567937,
9078098,
8947963,
3828084,
7430083,], columns = ['upc2'])
df2 = df2.astype(str)
pd.merge(df1, df2, left_on=df1['upc1'].astype(str).str[2:-2], right_on='upc2', how='inner')
Out[5]:
upc1 upc2
0 23456793749 4567937
1 78907809834 9078098
2 35894796324 8947963
3 67382808404 3828084
4 93743008374 7430083
Using str.extact, match all items in df1 with df2, then we using the result as merge key merge with df2
df1['keyfordf2']=df1.astype(str).upc.str.extract(r'({})'.format('|'.join(df2.upc.astype(str).tolist())),expand=True).fillna(False)
df1.merge(df2.astype(str),left_on='keyfordf2',right_on='upc')
Out[273]:
upc_x keyfordf2 upc_y
0 23456793749 4567937 4567937
1 78907809834 9078098 9078098
2 35894796324 8947963 8947963
3 67382808404 3828084 3828084
4 93743008374 7430083 7430083
You could make a new column in df1 and merge on that.
import pandas as pd
df1= pd.DataFrame({'upc': [ 23456793749, 78907809834, 35894796324, 67382808404, 93743008374]})
df2= pd.DataFrame({'upc': [ 4567937, 9078098, 8947963, 3828084, 7430083]})
df1['upc_old'] = df1['upc'] #in case you still need the old (longer) upc column
df1['upc'] = df1['upc'].astype(str).str[2:-2].astype(int)
merged_df = pd.merge(df1, df2, on='upc')
I want to iterate over df and change value in df2. If the value of A and B in df is equal to A and B in df2 then C in df is equal to C+E/2 of df2.
df = pd.read_csv('final.csv',names=['A','B','C','D'])
df2 = pd.read_csv('final.csv',names=['A','B','C','D','E'])
for x in df2:
z=x.loc['A','B']
df.loc['A','B']
a=df[['C']]
I have 2 dataframes, df1 and df2, and want to do the following, storing results in df3:
for each row in df1:
for each row in df2:
create a new row in df3 (called "df1-1, df2-1" or whatever) to store results
for each cell(column) in df1:
for the cell in df2 whose column name is the same as for the cell in df1:
compare the cells (using some comparing function func(a,b) ) and,
depending on the result of the comparison, write result into the
appropriate column of the "df1-1, df2-1" row of df3)
For example, something like:
df1
A B C D
foo bar foobar 7
gee whiz herp 10
df2
A B C D
zoo car foobar 8
df3
df1-df2 A B C D
foo-zoo func(foo,zoo) func(bar,car) func(foobar,foobar) func(7,8)
gee-zoo func(gee,zoo) func(whiz,car) func(herp,foobar) func(10,8)
I've started with this:
for r1 in df1.iterrows():
for r2 in df2.iterrows():
for c1 in r1:
for c2 in r2:
but am not sure what to do with it, and would appreciate some help.
So to continue the discussion in the comments, you can use vectorization, which is one of the selling points of a library like pandas or numpy. Ideally, you shouldn't ever be calling iterrows(). To be a little more explicit with my suggestion:
# with df1 and df2 provided as above, an example
df3 = df1['A'] * 3 + df2['A']
# recall that df2 only has the one row so pandas will broadcast a NaN there
df3
0 foofoofoozoo
1 NaN
Name: A, dtype: object
# more generally
# we know that df1 and df2 share column names, so we can initialize df3 with those names
df3 = pd.DataFrame(columns=df1.columns)
for colName in df1:
df3[colName] = func(df1[colName], df2[colName])
Now, you could even have different functions applied to different columns by, say, creating lambda functions and then zipping them with the column names:
# some example functions
colAFunc = lambda x, y: x + y
colBFunc = lambda x, y; x - y
....
columnFunctions = [colAFunc, colBFunc, ...]
# initialize df3 as above
df3 = pd.DataFrame(columns=df1.columns)
for func, colName in zip(columnFunctions, df1.columns):
df3[colName] = func(df1[colName], df2[colName])
The only "gotcha" that comes to mind is that you need to be sure that your function is applicable to the data in your columns. For instance, if you were to do something like df1['A'] - df2['A'] (with df1, df2 as you have provided), that would raise a ValueError as the subtraction of two strings is undefined. Just something to be aware of.
Edit, re: your comment: That is doable as well. Iterate over the dfX.columns that is larger, so you don't run into a KeyError, and throw an if statement in there:
# all the other jazz
# let's say df1 is [['A', 'B', 'C']] and df2 is [['A', 'B', 'C', 'D']]
# so iterate over df2 columns
for colName in df2:
if colName not in df1:
df3[colName] = np.nan # be sure to import numpy as np
else:
df3[colName] = func(df1[colName], df2[colName])