Tutorial by Examples

Group by one column Using the following DataFrame df = pd.DataFrame({'A': ['a', 'b', 'c', 'a', 'b', 'b'], 'B': [2, 8, 1, 4, 3, 8], 'C': [102, 98, 107, 104, 115, 87]}) df # Output: # A B C # 0 a 2 102 # 1 b 8 98 # 2 c 1 107 # 3 a...

For the following DataFrame: import numpy as np import pandas as pd np.random.seed(0) df = pd.DataFrame({'Age': np.random.randint(20, 70, 100), 'Sex': np.random.choice(['Male', 'Female'], 100), 'number_of_foo': np.random.randint(1, 20, 100)}) df.head() ...

When you do a groupby you can select either a single column or a list of columns: In [11]: df = pd.DataFrame([[1, 1, 2], [1, 2, 3], [2, 3, 4]], columns=["A", "B", "C"]) In [12]: df Out[12]: A B C 0 1 1 2 1 1 2 3 2 2 3 4 In [13]: g = df.groupby(...

The difference between size and count is: size counts NaN values, count does not. df = pd.DataFrame( {"Name":["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"], "City":["Seattle", &q...

In [1]: import numpy as np In [2]: import pandas as pd In [3]: df = pd.DataFrame({'A': list('XYZXYZXYZX'), 'B': [1, 2, 1, 3, 1, 2, 3, 3, 1, 2], 'C': [12, 14, 11, 12, 13, 14, 16, 12, 10, 19]}) In [4]: df.groupby('A')['B'].agg({'mean': np.mean, 'standard deviatio...

You can iterate on the object returned by groupby(). The iterator contains (Category, DataFrame) tuples. # Same example data as in the previous example. import numpy as np import pandas as pd np.random.seed(0) df = pd.DataFrame({'Age': np.random.randint(20, 70, 100), 'Sex':...

example: df = pd.DataFrame({'group1' : ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'group2' : ['C', 'C', 'C', 'D', 'E', 'E', 'F', 'F'], 'B' : ['one', np.NaN, np.NaN, np.NaN, ...