Python和pandas，groupby只在DataFrame列

Question

I would like to group some strings in the column called 'type' and insert them in a plotly bar, the problem is that from the new table created with groupby I can't extract the x and y to define them in the graph:

tipol1 = df.groupby(['tipology']).nunique()

tipol1

the outpot gives me tipology as index and the grouping based on how many times they repeat

         number data
typology  
  one     2      113
  two     33     33
  three   12     88
  four    44     888
  five    11     66

in the number column (in which I have other values it gives me the correct grouping of the tipology column) Also in the date column it gives me values (I think grouping the dates but not the dates in the correct format) I also found:

tipol=df.groupby(['tipology']).nunique()
tipol2 = tipol[['number']]
tipol2

to take only the number column, but nothing to do, I would need the tipology column (not in index) and the column with the tipology grouping numbers to get the x and y axis to import it into plotly!

One last try I made (making a big mess):

tipol=df.groupby(['tipology'],as_index=False).nunique()
tipol2 = tipol[['number']]


fig = go.Figure(data=[
go.Bar(name='test', x=df['tipology'], y=tipol2)

])

fig.update_layout(barmode='stack')
fig.show()

any suggestions thanks!

UPDATE

I would have too much code to give an example, it would be difficult for me and it would waste your time too. basically I would need a groupby with the addition of a column that would show the grouping value eg:

tipology    Date
home        10/01/18
home        11/01/18
garden      12/01/18
garden      12/01/18
garden      13/01/18
bathroom    13/01/18
bedroom     14/01/18
bedroom     15/01/18
kitchen     16/01/18
kitchen     16/01/18
kitchen     17/01/18

I wish this would happen: by deleting the date column and inserting the value column in the DataFrame that does the count

tipology   value
home         2
garden       3
bathroom     1
bedroom      2
kitchen      3

Then (I'm working with jupyer notebook) leaving the date column and adding the corresponding values to the value column based on their grouping:

  tipology       Date     value
   home        10/01/18     1
   home        11/01/18     1
   garden      12/01/18     2
   garden      12/01/18_____.
   garden      13/01/18     1
   bathroom    13/01/18     1
   bedroom     14/01/18     1
   bedroom     15/01/18     1
   kitchen     16/01/18     2
   kitchen     16/01/18_____.
   kitchen     17/01/18     1

I would need the columns to assign them to the x and y axes to import them to a graph! so none of the columns should be index

Answer 1

By default the method groupby will return a dataframe where the fields you are grouping on will be in the index of the dataframe. You can adjust this behaviour by setting as_index=False in the group by. Then tipology will still be a column in the dataframe that is returned:

tipol1 = df.groupby('tipology', as_index=False).nunique()