I have two dataframes, df1 and df2.
df1 contains integers and df2 contains booleans.
df1 and df2 are exactly the same size (like both are 10x10).
I would like to create a df3 that would take the data from df1 only if the value in the same location in df2 is True. All False would be replaced by Nan in df3
Thanks in advance!
As someone who is super new in merge/append on Python, I am trying to merge two different DF together.
DF1 has 2 columns with Text and ID columns and 100 rows
DF2 has 3 columns with Text, ID, and Match columns and has 20 rows
My goal is to combine the two DFs together so the "Match" column from DF2 can be merged into DF1.
The Match column is all "True" value, so when it gets merged over the other 80 rows on DF1 can be NaN and I can fix it later.
Thank you to everyone for the help and support!
Try a left merge using .merge(), like this:
DF_out = DF1.merge(DF2, on=['Text', 'ID'], how='left')
I have created a Pandas dataframe using:
df = pd.DataFrame(index=np.arange(140), columns=np.arange(20))
Which gives me an empty dataframe with 140 rows and 20 columns.
I have another dataframe with 120 columns and 20 rows, I call it df2. I would like to add these rows to fill df, but still retain the shape of 140x20.
When I use:
newdf = df.append(df2) I get a dataframe with 280 rows and 20 columns.
df.iloc[:len(df2), :] = df2.values
will do the job. As the no. of columns are same so we can safely do this. Other values in df will remain NaNs. This will update the df2 records at the beginning. If you want at the end, similarly, you can do df.iloc[-len(df2):, :] = df2.values
I have two dataframes df1 and df2 where df1 has 9 columns and df2 has 8 columns. I want to replace the first 8 columns of df1 with that of df2. How can this be done? I tried with iloc but not able to succeed.
Following are the files:
https://www.filehosting.org/file/details/842516/tpkA0t2vAtkrqKTb/df1.csv for df1
https://www.filehosting.org/file/details/842517/8XpizwCAX79p9rrZ/df2.csv for df2
import pandas as pd
df1=pd.DataFrame({0:[1,1,1,0,0,0],1:[0,1,0,0,0,0],2:[1,1,1,0,0,0],3:[0,0,0,2,3,4],4:[0,0,0,0,1,0],5:[0,0,0,2,1,2]})
df2=pd.DataFrame({6:[2,2,2,0,0,0],7:[0,2,0,0,0,0],8:[2,2,2,0,0,0],'d':[0,0,0,2,3,4],'e':[0,0,0,0,1,0],'f':[0,0,0,2,1,2]})
z=pd.concat([df1.iloc[:,3:],df2.iloc[:,0:3]],axis=1)
Here I have concatenated from 3rd column to last column of 1st dataframe and the first 3 column of 2nd dataframe. Similarly you concatenate whichever row or column you want to concatenate
I'm iterating over two separate dataframes, where one dataframe is a subset of the other. I need to ensure that only the columns in the set (df1) which are not contained in the subset (df2) pass the conditional statement.
In this case, it would be comparing the Series object during each iteration in df1 to the dataframe, df2. Ideally I would like to compare just the labels associated with each column, not the values contained in the columns. My code below. Any help would be greatly appreciated!
for i in df1:
for j in df2:
if df1[i] is not in df2:
...do some stuff between df1[i] and df2[j]
To find out if the values of df1 are in df2 you can use:
df1.isin(df2)
To find all values in df1 that are not in df2 you can use:
df1[~df1.isin(df2)]
The values that are in df1 and df2 will be a nan in this case