When you use df.apply(), each row of your DataFrame will be passed to your lambda function as a pandas Series. The frame's columns will then be the index of the series and you can access values using series[label].

So this should work:

df['D'] = (df.apply(lambda x: myfunc(x[colNames[0]], x[colNames[1]]), axis=1)) 

Use DataFrame.hist with columns in strings instead Series like data['msg_length']:

data.hist(column ='msg_length', by='label', bins=50)

I think I understood what you wanted to do and what you did not understand (mainly about the way to modifiy objects with pandas). I assume that you wanted to:

1. compute your aggregation by payment date in data
2. and then set its index to 'Payment date' field

Short answer: if you want to have this result into data, simply execute:

data = data.groupby('Payment date ')['Payment amount'].sum().to_frame()

'Payment date ' will be your new index, to_frame prevents your single column resulting dataframe to be squeezed into a pandas Series (which I think was your first intention to avoid, resetting your index to then set it back).

Let's dive into your code.

First line

data = data.groupby('Payment Date ')

First line is ok, but might not do exactly what you want. You are taking data, which I assume is a pandas DataFrame and reaffect it a pandas DataFrameGroupBy object. This kind of object does not hold any data, you can see it simply as a mapping between index(s) of your original DataFrame and associated groups (here, payment dates).

Anyway, you got your groupby object into data.

Second line

data['Payment Amount '].sum().reset_index()

This line does nothing. It shows the result of the computation in your Jupyter notebook, but nothing has been changed in data. data is still the same DataFrameGroupBy object.

Third line

data = data.set_index('Payment Date ', inplace  = True)

An exception is raised, saying that a DataFrameGroupBy objet has no set_index method. This is because data has not been changed by your second line of code. Even so, I would encourage you to avoid using inplace=True anytime in your code. You should always go with explicit reassignements.

Your code could look like (if you don't like the short answer above):

data = data.groupby('Payment date ')
data = data['Payment amount'].sum().reset_index()
data = data.set_index('Payment date ')  # No inplace=True!

The method .str is defined for data Series (eg. columns), but not DataFrames (tables). It requires an attribute series to be defined. In your case, in line 5 you slice the DataFrame with the 2nd column:

split_df[[2]]

This syntax returns a DataFrame with one column. Instead, you should be indexing and accessing the 2nd series:

split_df[2]

This returns a Series-type object, which has a series attribute and thus can be converted to string. This is also why df["Maths Formula"] works - it returns a Series object.

Check the version of your pandas library:

import pandas
print(pandas.__version__)

If your version is less than 0.24.1:

pip install --upgrade pandas

Looks like I was creating the dataframe with a wrong shape. The way it worked for me was to make sure the vertices were contained within a single object

finalLine_data = {'id': [1], 'geometry':[LineString(lineCoords)]}
gp_df = gpd.GeoDataFrame(finalLine_data, crs=crs)
gp_df['geometry'].to_file(path_to_file, driver="ESRI Shapefile")

The finalLine_data looks like this

{'id': [1], 'geometry': [<shapely.geometry.linestring.LineString object at 0x7f42af79c290>]}
   id                                           geometry
0   1  LINESTRING (500191.600 7656332.248, 500223.849...