Could you tell me how to group a table (from products1.txt file) like following:
Age;Name;Country
10;Valentyn;Ukraine
12;Igor;Russia
12;Valentyn;
10;Valentyn;Russia
So I can find out how many Valentyns have an empty "Country" cell.
I ran the following code:
import pandas as pd
df = pd.read_csv('d:\products1.txt', sep = ";")
result = df[(df["Name"] == "Valentyn") & (df["Country"] == None)]
But I get an error...
You should use isnull (rather than == None) to check for NaN:
In [11]: df[(df.Country.isnull()) & (df.Name == 'Valentyn')]
Out[11]:
Age Name Country
2 12 Valentyn NaN
Another option would be to check those which had Country NaN and then count the values:
In [12]: df.Name[df.Country.isnull()]
Out[12]:
2 Valentyn
Name: Name, dtype: object
In [13]: df.Name[df.Country.isnull()].value_counts()
Out[13]:
Valentyn 1
dtype: int64