In [1]: df = pd.DataFrame({'A': [5,6,3,4], 'B': [1,2,3,5]})
In [2]: df
Out[2]:
A B
0 5 1
1 6 2
2 3 3
3 4 5
In [3]: df[df['A'].isin([3, 6])]
Out[3]:
A B
1 6 2
2 3 3//To select rows whose column value equals a scalar, some_value, use ==:
df.loc[df['A'] == 'foo']
//To select rows whose column value is in an iterable, some_values, use isin:
df.loc[df['B'].isin(['one','three'])]
//Combine multiple conditions with "&" :
df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)]select_color = df.loc[df['Color'] == 'Green']#To select rows whose column value is in an iterable array, which we'll define as array, you can use isin:
array = ['yellow', 'green']
df.loc[df['favorite_color'].isin(array)]