Python：如何查找一列中的哪些值在另一特定列（数据框）中具有NaN值

Question

Suppose we have df1 that looks like this:

x1 = [{'partner': "Afghanistan", 'commodity': NaN}, 
      {'partner': "Zambia",      'commodity': 2}, 
      {'partner': "Germany",     'commodity': 2},
      {'partner': "Afghanistan", 'commodity': NaN},
      {'partner': "Canada",      'commodity': NaN},
      {'partner': "Italy",       'commodity': 3},
      {'partner': "Canada",      'commodity': NaN},
      {'partner': "USA",         'commodity': NaN}]

df1 = pd.DataFrame(x1)

What I want to do is see the list of values in partner that have the NaN value in commodity , but I don't want to have the same partner listed twice.

So my preferred result would look like this:

commodity_nan_partners=
Afghanistan
Canada
USA

and not:

Afghanistan
Afghanistan
Canada
Canada
USA

Answer 1

You can look for NaN values using isnull , then get unique values with unique or set :

>>> pd.Series(df1.loc[df1.commodity.isnull(),'partner'].unique())
0    Afghanistan
1         Canada
2            USA
dtype: object

# or
>>> pd.Series(list(set(df1.loc[df1.commodity.isnull(),'partner'])))
0         Canada
1    Afghanistan
2            USA
dtype: object

Answer 2

loc + isnull + drop_duplicates

You can filter your series and then drop duplicates:

res = df1.loc[df1['commodity'].isnull(), 'partner'].drop_duplicates()

print(res)

0    Afghanistan
4         Canada
7            USA
Name: partner, dtype: object

Answer 3

Step 1
Filter out to retain valid strings only:

v = df1.loc[df1.commodity.isna(), 'partner']

Or,

v = df1.partner[df1.commodity.isna()]

print(v)
0    Afghanistan
3    Afghanistan
4         Canada
6         Canada
7            USA
Name: partner, dtype: object

Step 2
Drop duplicates.

If you want a collection,

ingredients.unique()
array(['Afghanistan', 'Canada', 'USA'], dtype=object)

Or,

set(ingredients)
{'Afghanistan', 'Canada', 'USA'}

If you want a Series,

ser = ingredients.drop_duplicates().reset_index(drop=True)

0    Afghanistan
1         Canada
2            USA
Name: partner, dtype: object

If you want a DataFrame,

df = ser.to_frame()

Answer 4

May check with dropna , just provide a different Idea here .

set(df1.partner.tolist())-set(df1.dropna().partner.tolist())
Out[94]: {'Afghanistan', 'Canada', 'USA'}

Answer 5

Just another alternatives:

>>> df1[df1.isnull().any(axis=1)]['partner'].drop_duplicates()
0    Afghanistan
4         Canada
7            USA
Name: partner, dtype: object

Using loc + np.isnan

>>> df1.loc[np.isnan(df1.commodity), 'partner'].drop_duplicates()
0    Afghanistan
4         Canada
7            USA
Name: partner, dtype: object