I have the following sample DF:

I have 2 calculations

1. Calculate the number of blank rows in the sales column

   missingSalesInfo = DF['Sales'].isnull().sum()

   missingSalesInfo = ("Number of missing values: ",missingSalesInfo)

2. Calculate the total car sales

totalSales = DF['Sales'].sum()

totalSales = ("Total car sales: ",totalSales)

What I want to do is create a new DF, lets call it DF2 to store the above results. See example below

DF2

CodePudding user response：

Use Series.agg with aggregate functions in dictionary, convert to integers and convert to DataFrame from Series by Series.reset_index, for set new columns names is used DataFrame.set_axis:

df2 = (df['Sales'].agg({'Number of missing values': lambda x: x.isna().sum(),
                        'Total car sales': 'sum'})
                  .astype(int)
                  .reset_index()
                  .set_axis(['Description','Results'], axis=1)
                 )
print (df2)
                Description  Results
0  Number of missing values        1
1           Total car sales   150000

Alternative:

df2 = (df['Sales'].agg({'Number of missing values': lambda x: x.isna().sum(),
                       'Total car sales': 'sum'})
                  .astype(int)
                  .reset_index())

df2.columns = ['Description','Results']

CodePudding user response：

That's just a question of how to create a DataFrame. You can do that in a few ways, but here it's done with dictionary:

df2 = pd.DataFrame({
    'Description': ['Number of missing values', 'Total car sales'], 
    'Results': [DF['Sales'].isnull().sum(), DF['Sales'].sum()]
})

CodePudding user response：

Several ways to do that. Here's a simple way.

df_2 = pd.DataFrame()
df_2['Number of missing values'] =  DF['Sales'].isnull().sum()
df_2['Total car sales'] =  DF['Sales'].sum()`