pandas count specific value in column
(df[education]=='9th').sum()pandas count specific value in column
(df[education]=='9th').sum()pandas count occurrences in column
# Basic syntax:
df['column'].value_counts()
# Get normalized counts:
df['column'].value_counts(normalize=True)
# Example usage:
# Make dataframe
import pandas as pd
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 5, 9]]),
columns=['a', 'b', 'c'])
print(df)
a b c
0 1 2 3
1 4 5 6
2 7 5 9
df['b'].value_counts() # Returns:
5 2 # 5 appears twice in column 'b'
2 1
df['b'].value_counts(normalize=True) # Returns:
5 0.666667 # 5 accounts for 2/3 of the entries in column 'b'
2 0.333333python - count number of occurence in a column
print df
col1 education
0 a 9th
1 b 9th
2 c 8th
len(df[df['education'] == '9th'])count specific instances in a columb in pandas
df.describe(include=['O']) # give count of unieque categoricalpandas count occurrences of certain value in row
print df
col1 education
0 a 9th
1 b 9th
2 c 8th
print df.education == '9th'
0 True
1 True
2 False
Name: education, dtype: bool
print df[df.education == '9th']
col1 education
0 a 9th
1 b 9th
print df[df.education == '9th'].shape[0]
2
print len(df[df['education'] == '9th'])
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