We Keep Coding

Merge on substring

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

concat by taking the values from column

i have a list ['df1', 'df2'] where i have stores some dataframes which have been filtered on few conditions. Then i have converted this list to dataframe using
df = pd.DataFrame(list1)
now the df has only one column
0
df1
df2
sometimes it may also have
0
df1
df2
df3
i wanted to concate all these my static code is
df_new = pd.concat([df1,df2],axis=1) or
df_new = pd.concat([df1,df2,df3],axis=1)
how can i make it dynamic (without me specifying as df1,df2) so that it takes the values and concat it.

Using array to add the lists and data frames :
import pandas as pd
lists = [[1,2,3],[4,5,6]]
arr = []
for l in lists:
new_df = pd.DataFrame(l)
arr.append(new_df)
df = pd.concat(arr,axis=1)
df
Result :
0 0
0 1 4
1 2 5
2 3 6

Union of two pandas DataFrames

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

Mapping to dataframes based on one column

I have a dataframe (df1) of 5 columns (a,b,c,d,e) with 6 rows and another dataframe (df2) with 2 columns (a,z) with 20000 rows.
How do I map and merge those dataframes using ('a') value.
So that df1 having 5 columns should map values in df2 having 2 columns with 'a' value and return a new df which has 6 columns (5 from df1 and 1 mapped row in df2) with 6 rows.

By using pd.concat:
import pandas as pd
import numpy as np
columns_df1 = ['a','b','c','d']
columns_df2 = ['a','z']
data_df1 = [['abc','def','ghi','xyz'],['abc2','def2','ghi2','xyz2'],['abc3','def3','ghi3','xyz3'],['abc4','def4','ghi4','xyz4']]
data_df2 = [['a','z'],['a2','z2']]
df_1 = pd.DataFrame(data_df1, columns=columns_df1)
df_2 = pd.DataFrame(data_df2, columns=columns_df2)
print(df_1)
print(df_2)
frames = [df_1, df_2]
print (pd.concat(frames))
OUTPUT:
Edit:
To replace NaN values you could use pandas.DataFrame.fillna:
print (pd.concat(frames).fillna("NULL"))
Replcae NULL with anything you want e.g. 0
OUTPUT:

Compare 2 Pandas dataframes, row by row, cell by cell

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])