I want to apply a group by on a pandas dataframe. I want to group by three columns and calculate their count. I used the following code
data.groupby(['post_product_list','cust_visid','date_time']).count()
But it didn't seem to work
data.groupby(['post_product_list','cust_visid','date_time']).size()
Related
I use groupby method to group data by month. The ouput is exactly want I wanted.
What I want to understand is, how does x display only 3 columns (Quantity Ordered, Price Each and Sales) and rejects the other columns shown in the dataset after I use the groupby method. Is it because the other data isn't numeric ? Is it because I used sum method along with groupby method ?
Since sum is a numeric function, pandas would only apply it to the columns that are numeric. This is described in the documentation as Automatic exclusion of “nuisance” columns.
I am tyring to write a function that takes a dataframe, groups the dataframe by a column, and then orders that column by from largest to smallest using the average of a second column. I am trying to return a dataframe. I am using both seaborn and pandas.
This what I have so far
def table(df, columnone, columntwo):
dfnew = df.groupby([columnone])[columntwo].nlargest()
return dfnew
I am not very sure what I am missing or what I should be looking for. I am pretty new with python and any help would be appreciated.
I think you are looking for this:
def table(df, columnone, columntwo):
return df.groupby([columnone])\
.mean()\
.sort_values(by=[columntwo], ascending=False)
Here groupby will create the groups, mean will average the values in other columns, sort_values will sort the resulting dataframe created after applying groupby.
I have a df having a single column containing rows of repeating data. I want to display a pivot table of unique values of that column along with their count. I know it would be some sort of groupby however I could not get it to work, please help.
.
Try:
df.groupby("PdDistrict").size()
I have this issues: I have to sum all the values present in a dataframe column based on the value that I have in another column. In the specific I have a column "App" and a columns "n_of_Installs".
What I need is count all "n. of Installs" for each App.
I tried this code: dataframe.groupby('App').sum()['n_of_Installs'] but it doesn't work.
Following line will do what you want:
dataframe.groupby('App')['n_of_installs'].sum() # returns pandas Series
Note that above code returns you a pandas Series. If you wish to get a pandas DataFrame instead, use the as_index=False option of groupby
dataframe.groupby('App', as_index=False)['n_of_installs'].sum() # returns pandas DataFrame
I'm having a Pandas Dataframe and I'm doing a groupby on two columns and have a couple of aggregate functions on a column. Here is how my code looks like
df2 = df[X,Y, Z].groupby([X,Y]).agg([np.mean, np.max, np.min]).reset_index()
It find the aggregate functions on the column Z.
I need to sort by let's say min (i.e. sort_values('min')) column but it keeps complaining that 'min' column does not exist. How can I do that
Since you are generating a pd.MultiIndex, you must use a tuple in sort_values.
Try:
df2.sort_values(('Z','amin'))