We Keep Coding

How to apply multiple lambda functions for a single column?

I'm working on a project where I'm would like to use 2 lambda functions to find a match in another column. I created a dummy df with the following code below:
df = pd.DataFrame(np.random.randint(0,10,size=(100, 4)), columns=list('ABCD'))
Now I would like to find column A matches in column B.
df['match'] = df.apply(lambda x: x['B'].find(x['A']), axis=1).ge(0)
Now I would like to add an extra check where I'm also checking if column C values appear in column D:
df['match'] = df.apply(lambda x: x['D'].find(x['C']), axis=1).ge(0)
I'm searching for a solution where I can combine these 2 lines of code that is a one-liner that could be combined with an '&' operator for example. I hope this helps.

You can use and operator instead.
df['match'] = df.apply(lambda x: (x['B'] == x['A']) and (x['D'] == x['C']), axis=1).ge(0)

Pandas chaining only modifying one column in dataframe using apply or pipe

I'm trying to clean a file using pandas chaining and I have come to a point where I only need to clean one column and leave the rest as is, is there a way to accomplished this using pandas chaining using apply or pipe.
I have tried the following which works, but I only would like to replace the dash in one specific column and leave the the rest as is since the dash in other columns is appropriate.
df = (dataFrame
.dropna()
.pipe(lambda x: x.replace("-", "", regex=True))
)
I have also tried this, which doesn't work since it only returns the seatnumber column.
df = (dataFrame
.dropna()
.pipe(lambda x: x['seatnumber'].replace("-", "", regex=True))
)
Thanks is advance.

One way is to assign a column with the same name of the column of interest:
df = (dataFrame
.dropna()
.assign(seatnumber=lambda x: x.seatnumber.replace("-", "", regex=True))
)
where the dataframe at that point is passed to the lambda as x.

Let us pass a dict
df = (dataFrame
.dropna().replace({"seatnumber" : {"-":""}}, regex=True)
)

Dropping index in DataFrame for CSV file

Working with a CSV file in PyCharm. I want to delete the automatically-generated index column. When I print it, however, the answer I get in the terminal is "None". All the answers by other users indicate that the reset_index method should work.
If I just say "df = df.reset_index(drop=True)" it does not delete the column, either.
import pandas as pd
df = pd.read_csv("music.csv")
df['id'] = df.index + 1
cols = list(df.columns.values)
df = df[[cols[-1]]+cols[:3]]
df = df.reset_index(drop=True, inplace=True)
print(df)

I agree with #It_is_Chris. Also,
This is not true because return is None:
df = df.reset_index(drop=True, inplace=True)
It's should be like this:
df.reset_index(drop=True, inplace=True)
or
df = df.reset_index(drop=True)

Since you said you're trying to "delete the automatically-generated index column" I could think of two solutions!
Fist solution:
Assign the index column to your dataset index column. Let's say your dataset has already been indexed/numbered, then you could do something like this:
#assuming your first column in the dataset is your index column which has the index number of zero
df = pd.read_csv("yourfile.csv", index_col=0)
#you won't see the automatically-generated index column anymore
df.head()
Second solution:
You could delete it in the final csv:
#To export your df to a csv without the automatically-generated index column
df.to_csv("yourfile.csv", index=False)

Merging Columns in such a way that rows with duplicate elements are separated by commas

This is the input dataframe that I'm having.
And this is the output that I want:
As you can see, both the dataframes are merged on the column Key1 in such a way that the row with common elements are separated by commas.
I have tried using merge but it doesn't give the correct output.
mer = pd.merge(df,df, on='Key1', how='inner')
Is there a specific way to approach this?

You can convert values to strings and join unique values by , in custom lambda function:
Solution for working with missing values in Key1 in oldier pandas versions with replace by temp values:
df1 = (df.fillna({'Key1': 'missing'})
.groupby('Key1')
.agg(lambda x: ','.join(pd.unique(x.astype(str))))
.reset_index()
.replace({'Key1':{'missing':np.nan}}))
Solution for last pandas versions:
df1 = (df.groupby('Key1')
.agg(lambda x: ','.join(pd.unique(x.astype(str))))
.reset_index())

How can I delete a row in a Pandas dataframe if the entire row is null?

I have been checking each value of each row and if all of them are null, I delete the row with something like this:
df = pandas.concat([df[:2], df[3:]])
But, I am thinking there's got to be a better way to do this. I have been trying to use a mask or doing something like this:
rows_to_keep = df.apply(
lambda row :
any([if val is None for val in row ])
, axis=1)
I also tried something like this (suggested on another stack overflow question)
pandas.DataFrame.dropna()
but don't see any differences in my printed dataframe.

dropna returns a new DataFrame, you probably just want:
df = df.dropna()
or
df.dropna(inplace=True)
If you have a more complicated mask, rows_to_keep, you can do:
df = df[rows_to_keep]