CODEWITHSUNDEEP
pythonpandas
VPN
VPN

Reputation: 113

Displaying and adding column to a dataframe in pandas

I have created a dataframe called date as follows, but I am not able to call that dataframe by typing date in further cells. I need to call that dataframe as a table and add new column to it.

Right now, this is what I have:

date = data.groupby(pd.to_datetime(data['Completion Date'], format='%d.%m.%Y').dt.month)['Learning    Hours'].sum()
date.index = pd.to_datetime(date.index, format='%m').month_name().str[:3]
date.rename_axis('Month').reset_index(name='Learning hours')

O/P: 

Month   Learning hours

Jan     349.5

Feb     417.0

I need to add a new column to this table called Required, with values 430 in all rows as shown in the below:

needed O/P: 

Month   Learning hours  Required

Jan     349.5           430

Feb     417.0           430

Upvotes: 1

Views: 38

Answers (1)

jezrael
jezrael

Reputation: 863741

I think here is necessary assign output back to date variable and then create new colum:

date = data.groupby(pd.to_datetime(data['Completion Date'], format='%d.%m.%Y').dt.month)['Learning Hours'].sum() 
date.index = pd.to_datetime(date.index, format='%m').month_name().str[:3] 
date = date.rename_axis('Month').reset_index(name='Learning hours')
date['Required'] = 430

You can simplify solution if possible lowercases first 3 letters of months names:

months = (pd.to_datetime(data['Completion Date'], format='%d.%m.%Y')
            .dt.strftime('%b')
            .rename('Month'))
date = (data.groupby(months, sort=False)['Learning Hours']
            .sum()
            .reset_index(name='Learning hours'))
date['Required'] = 430

Or:

months = (pd.to_datetime(data['Completion Date'], format='%d.%m.%Y')
            .dt.strftime('%b')
            .rename('Month'))
date = (data.groupby(months, sort=False)['Learning Hours']
            .sum()
            .reset_index(name='Learning hours')
            .assign(Required = 430))

Upvotes: 2

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