Develop Reference

How to collapse all rows in pandas dataframe across all columns

I am trying to collapse all the rows of a dataframe into one single row across all columns.
My data frame looks like the following:
name
job
value
bob
business
100
NAN
dentist
Nan
jack
Nan
Nan
I am trying to get the following output:
name
job
value
bob jack
business dentist
100
I am trying to group across all columns, I do not care if the value column is converted to dtype object (string).
I'm just trying to collapse all the rows across all columns.
I've tried groupby(index=0) but did not get good results.

You could apply join:
out = df.apply(lambda x: ' '.join(x.dropna().astype(str))).to_frame().T
Output:
name job value
0 bob jack business dentist 100.0

Try this:
new_df = df.agg(lambda x: x.dropna().astype(str).tolist()).str.join(' ').to_frame().T
Output:
>>> new_df
name job value
0 bob jack business dentist 100.0

Update an existing column in one dataframe based on the value of a column in another dataframe

I have two csv files as my raw data to read into different dataframes. One is called 'employee' and another is called 'origin'. However, I cannot upload the files here so I hardcoded the data into the dataframes below. The task I'm trying to solve is to update the 'Eligible' column in employee_details with 'Yes' or 'No' based on the value of the 'Country' column in origin_details. If Country = UK, then put 'Yes' in the Eligible column for that Personal_ID. Else, put 'No'.
import pandas as pd
import numpy as np
employee = {
'Personal_ID': ['1000123', '1100258', '1104682', '1020943'],
'Name': ['Tom', 'Joseph', 'Krish', 'John'],
'Age': ['40', '35', '43', '51'],
'Eligible': ' '}
origin = {
'Personal_ID': ['1000123', '1100258', '1104682', '1020943', '1573482', '1739526'],
'Country': ['UK', 'USA', 'FRA', 'SWE', 'UK', 'AU']}
employee_details = pd.DataFrame(employee)
origin_details = pd.DataFrame(origin)
employee_details['Eligible'] = np.where((origin_details['Country']) == 'UK', 'Yes', 'No')
print(employee_details)
print(origin_details)
The output of above code shows the below error message:
ValueError: Length of values (6) does not match length of index (4)
However, I am expecting to see the below as my output.
Personal_ID Name Age Eligible
0 1000123 Tom 40 Yes
1 1100258 Joseph 35 No
2 1104682 Krish 43 No
3 1020943 John 51 No
I also don't want to delete anything in my dataframes to match the size specified in the ValueError message because I may need the extra Personal_IDs in the origin_details later. Alternatively, I can keep all the existing Personal_ID's in the raw data (employee_details, origin_details) and create a new dataframe from those to extract the records which have the same Personal_ID's and determine the np.where() condition from there.
Please advise! Any helps are appreciated, thank you!!

You can merge the 2 dataframes on Personal ID and then use np.where
Merge with how='outer' to keep all personal IDs
df_merge = pd.merge(employee_details, origin_details, on='Personal_ID', how='outer')
df_merge['Eligible'] = np.where(df_merge['Country']=='UK', 'Yes', 'No')
Personal_ID Name Age Eligible Country
0 1000123 Tom 40 Yes UK
1 1100258 Joseph 35 No USA
2 1104682 Krish 43 No FRA
3 1020943 John 51 No SWE
4 1573482 NaN NaN Yes UK
5 1739526 NaN NaN No AU
If you dont want to keep all personal IDs then you can merge with how='inner' and you won't see the NANs
df_merge = pd.merge(employee_details, origin_details, on='Personal_ID', how='inner')
df_merge['Eligible'] = np.where(df_merge['Country']=='UK', 'Yes', 'No')
Personal_ID Name Age Eligible Country
0 1000123 Tom 40 Yes UK
1 1100258 Joseph 35 No USA
2 1104682 Krish 43 No FRA
3 1020943 John 51 No SWE

You are using a Pandas Series object inside a Numpy method, np.where((origin_details['Country'])). I believe this is the problem.
try:
employee_details['Eligible'] = origin_details['Country'].apply(lambda x:"Yes" if x=='UK' else "No")
It is always much easier and faster to use the pandas library to analyze dataframes instead of converting them back to numpy arrays

Well, the first thing I want to answer about is the exception and how lucky you are that it didn't if your tables were the same length your code was going to work.
but there is an assumption in the code that I don't think you thought about and that is that the ids may not be in the same order or like in the example there are more ids in some table than the other if you had the same length of tables but not the same order you would have got incorrect eligible values for each row. the current way to do this is as follow
first join the table to one using personal_id but use left join as you don't want to lose data if there is no origin info for that personal id.
combine_df = pd.merge(employee_details, origin_details, on='Personal_ID', how='left')
use the apply function to fill the new column
combine_df['Eligible'] = combine_df['Country'].apply(lambda x:'Yes' if x=='UK' else 'No')

Check if pandas column contains text in another dataframe and replace values

I have two df's, one for user names and another for real names. I'd like to know how I can check if I have a real name in my first df using the data of the other, and then replace it.
For example:
import pandas as pd
df1 = pd.DataFrame({'userName':['peterKing', 'john', 'joe545', 'mary']})
df2 = pd.DataFrame({'realName':['alice','peter', 'john', 'francis', 'joe', 'carol']})
df1
userName
0 peterKing
1 john
2 joe545
3 mary
df2
realName
0 alice
1 peter
2 john
3 francis
4 joe
5 carol
My code should replace 'peterKing' and 'joe545' since these names appear in my df2. I tried using pd.contains, but I can only verify if a name appears or not.
The output should be like this:
userName
0 peter
1 john
2 joe
3 mary
Can someone help me with that? Thanks in advance!

You can use loc[row, colum], here you can see the documentation about loc method. And Series.str.contain method to select the usernames you need to replace with the real names. In my opinion, this solution is clear in terms of readability.
for real_name in df2['realName'].to_list():
df1.loc[ df1['userName'].str.contains(real_name), 'userName' ] = real_name
Output:
userName
0 peter
1 john
2 joe
3 mary

How to drop rows in one DataFrame based on one similar column in another Dataframe that has a different number of rows

I have two DataFrames that are completely dissimilar except for certain values in one particular column:
df
First Last Email Age
0 Adam Smith email1#email.com 30
1 John Brown email2#email.com 35
2 Joe Max email3#email.com 40
3 Will Bill email4#email.com 25
4 Johnny Jacks email5#email.com 50
df2
ID Location Contact
0 5435 Austin email5#email.com
1 4234 Atlanta email1#email.com
2 7896 Toronto email3#email.com
How would I go about finding the matching values in the Email column of df and the Contact column of df2, and then dropping the whole row in df based on that match?
Output I'm looking for (index numbering doesn't matter):
df1
First Last Email Age
1 John Brown email2#email.com 35
3 Will Bill email4#email.com 25
I've been able to identify matches using a few different methods like:
Changing the column names to be identical
common = df.merge(df2,on=['Email'])
df3 = df[(~df['Email'].isin(common['Email']))]
But df3 still shows all the rows from df.
I've also tried:
common = df['Email'].isin(df2['Contact'])
df.drop(df[common].index, inplace = True)
And again, identifies the matches but df still contains all original rows.
So the main thing I'm having difficulty with is updating df with the matches dropped or creating a new DataFrame that contains only the rows with dissimilar values when comparing the Email column from df and the Contact column in df2. Appreciate any suggestions.

As mentioned in the comments(#Arkadiusz), it is enough to filter your data using the following
df3 = df[(~df['Email'].isin(df2.Contact))].copy()
print(df3)

Update missing values in a column using pandas

I have a dataframe df with two of the columns being 'city' and 'zip_code':
df = pd.DataFrame({'city': ['Cambridge','Washington','Miami','Cambridge','Miami',
'Washington'], 'zip_code': ['12345','67891','23457','','','']})
As shown above, a particular city contains zip code in one of the rows, but the zip_code is missing for the same city in some other row. I want to fill those missing values based on the zip_code values of that city in some other row. Basically, wherever there is a missing zip_code, it checks zip_code for that city in other rows, and if found, fills the value for zip_code.If not found, fills 'NA'.
How do I accomplish this task using pandas?

You can go for:
import numpy as np
df['zip_code'] = df.replace(r'', np.nan).groupby('city')['zip_code'].fillna(method='ffill').fillna(method='bfill')
>>> df
city zip_code
0 Cambridge 12345
1 Washington 67891
2 Miami 23457
3 Cambridge 12345
4 Miami 23457
5 Washington 67891

You can check the string length using str.len and for those rows, filter the main df to those with valid zip_codes, set the index to those and call map on the 'city' column which will perform the lookup and fill those values:
In [255]:
df.loc[df['zip_code'].str.len() == 0, 'zip_code'] = df['city'].map(df[df['zip_code'].str.len() == 5].set_index('city')['zip_code'])
df
Out[255]:
city zip_code
0 Cambridge 12345
1 Washington 67891
2 Miami 23457
3 Cambridge 12345
4 Miami 23457
5 Washington 67891
If your real data has lots of repeating values then you'll need to additionally call drop_duplicates first:
df.loc[df['zip_code'].str.len() == 0, 'zip_code'] = df['city'].map(df[df['zip_code'].str.len() == 5].drop_duplicates(subset='city').set_index('city')['zip_code'])
The reason you need to do this is because it'll raise an error if there are duplicate index entries

My suggestion would be to first create a dictonary that maps from the city to the zip code. You can create this dictionary from the one DataFrame.
And then you use that dictionary to fill in all missing zip code values.