set multi index of an existing data frame in pandas

Question

I have a DataFrame that looks like

  Emp1    Empl2           date       Company 0    0        0     2012-05-01         apple 1    0        1     2012-05-29         apple 2    0        1     2013-05-02         apple 3    0        1     2013-11-22         apple 18   1        0     2011-09-09        google 19   1        0     2012-02-02        google 20   1        0     2012-11-26        google 21   1        0     2013-05-11        google 

I want to pass the company and date for setting a MultiIndex for this DataFrame. Currently it has a default index. I am using df.set_index(['Company', 'date'], inplace=True)

df = pd.DataFrame() for c in company_list:         row = pd.DataFrame([dict(company = '%s' %s, date = datetime.date(2012, 05, 01))])         df = df.append(row, ignore_index = True)         for e in emp_list:             dataset  = pd.read_sql("select company, emp_name, date(date), count(*) from company_table where  = '"+s+"' and emp_name = '"+b+"' group by company, date, name LIMIT 5 ", con)                 if len(dataset) == 0:                 row = pd.DataFrame([dict(sitename='%s' %s, name = '%s' %b, date = datetime.date(2012, 05, 01), count = np.nan)])                 dataset = dataset.append(row, ignore_index=True)             dataset = dataset.rename(columns = {'count': '%s' %b})             dataset = dataset.groupby(['company', 'date', 'emp_name'], as_index = False).sum()              dataset = dataset.drop('emp_name', 1)             df = pd.merge(df, dataset, how = '')             df = df.sort('date', ascending = True)             df.fillna(0, inplace = True)  df.set_index(['Company', 'date'], inplace=True)             print df 

But when I print this DataFrame, it prints None. I saw this solution from stackoverflow it self. Is this not the correct way of doing it. Also I want to shuffle the positions of the columns company and date so that company becomes the first index, and date becomes the second in Hierarchy. Any ideas on this?

Answer 1

When you pass inplace in makes the changes on the original variable and returns None, and the function does not return the modified dataframe, it returns None.

is_none = df.set_index(['Company', 'date'], inplace=True) df  # the dataframe you want is_none # has the value None 

so when you have a line like:

df = df.set_index(['Company', 'date'], inplace=True) 

it first modifies df... but then it sets df to None!

That is, you should just use the line:

df.set_index(['Company', 'date'], inplace=True)