pandas count occurrences in column

1(df[education]=='9th').sum()
2

1# Basic syntax:
2df['column'].value_counts()
3
4# Get normalized counts:
5df['column'].value_counts(normalize=True)
6
7# Example usage:
8# Make dataframe
9import pandas as pd
10df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 5, 9]]), 
11                  columns=['a', 'b', 'c'])
12
13print(df)
14   a  b  c
150  1  2  3
161  4  5  6
172  7  5  9
18
19df['b'].value_counts() # Returns:
205    2 # 5 appears twice in column 'b'
212    1
22
23df['b'].value_counts(normalize=True) # Returns:
245    0.666667 # 5 accounts for 2/3 of the entries in column 'b'
252    0.333333

1print df
2  col1 education
30    a       9th
41    b       9th
52    c       8th
6
7len(df[df['education'] == '9th'])

1df.describe(include=['O']) # give count of unieque categorical 

1print df
2  col1 education
30    a       9th
41    b       9th
52    c       8th
6
7print df.education == '9th'
80     True
91     True
102    False
11Name: education, dtype: bool
12
13print df[df.education == '9th']
14  col1 education
150    a       9th
161    b       9th
17
18print df[df.education == '9th'].shape[0]
192
20print len(df[df['education'] == '9th'])
212