Pandas - Conditional drop duplicates based on number of NaN - python-3.x

Question

I have a Pandas 0.24.2 dataframe for Python 3.7x as below. I want to drop_duplicates() with the same Name based on a conditional logic. A similar question can be found here: Pandas - Conditional drop duplicates but it gets more complicated in my case
import pandas as pd
import numpy as np
df = pd.DataFrame({
'Id': [1, 2, 3, 4, 5, 6 ],
'Name': ['A', 'B', 'C', 'A', 'B', 'C' ],
'Value1':[1, np.NaN, 0, np.NaN, 1, np.NaN],
'Value2':[np.NaN, 0, np.NaN, 1, np.NaN, 0 ],
'Value3':[np.NaN, 0, np.NaN, 1, np.NaN, np.NaN]
})
How is it possible to:
Drop duplicates for same 'Name' records, keeping the one that has less NaNs?
If they have the same number of NaNs, keeping the one that has NOT a NaN in 'Value1'?
The desired output would be:
Id Name Value1 Value2 Value3
2 2 B NaN 0 0
3 3 C 0 NaN NaN
4 4 A NaN 1 1

Answer 1

Idea is create helper columns for both conditions, sorting and remove duplicates:
df1 = df.assign(count= df.isna().sum(axis=1),
count_val1 = df['Value1'].isna().view('i1'))
df2 = (df1.sort_values(['count', 'count_val1'])[df.columns]
.drop_duplicates('Name')
.sort_index())
print (df2)
Id Name Value1 Value2 Value3
1 2 B NaN 0.0 0.0
2 3 C 0.0 NaN NaN
3 4 A NaN 1.0 1.0

Answer 2

Here is a different solution. The goal is to create two columns that help sort the duplicate rows that will be deleted.
First, we create the columns.
df['count_nan'] = df.isnull().sum(axis=1)
Value1_nan = []
for row in df['Value1']:
if row >= 0:
Value1_nan.append(0)
else:
Value1_nan.append(1)
df['Value1_nan'] = Value1_nan
We then sort the columns so that the column with the most NaNs appears first.
df.sort_values(by=['Name','count_nan', 'Value1'], inplace=True, ascending = [True, True, True])
Finally, we drop the "last" duplicate line. That is, we keep the line with the smallest number of NaNs followed by the line with the smallest number of NaNs in Value1
df = df.drop_duplicates(subset = ['Name'],keep='first')