Python Pandas Counting the Occurrences of a Specific value

I am trying to find the number of times a certain value appears in one column.

I have made the dataframe with data = pd.DataFrame.from_csv('data/DataSet2.csv')

and now I want to find the number of times something appears in a column. How is this done?

I thought it was the below, where I am looking in the education column and counting the number of time ? occurs.

The code below shows that I am trying to find the number of times 9th appears and the error is what I am getting when I run the code

Code

missing2 = df.education.value_counts()['9th']
print(missing2)

Error

KeyError: '9th'

You can create subset of data with your condition and then use shape or len:

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'])
2

Performance is interesting, the fastest solution is compare numpy array and sum:

Code:

import perfplot, string
np.random.seed(123)


def shape(df):
    return df[df.education == 'a'].shape[0]

def len_df(df):
    return len(df[df['education'] == 'a'])

def query_count(df):
    return df.query('education == "a"').education.count()

def sum_mask(df):
    return (df.education == 'a').sum()

def sum_mask_numpy(df):
    return (df.education.values == 'a').sum()

def make_df(n):
    L = list(string.ascii_letters)
    df = pd.DataFrame(np.random.choice(L, size=n), columns=['education'])
    return df

perfplot.show(
    setup=make_df,
    kernels=[shape, len_df, query_count, sum_mask, sum_mask_numpy],
    n_range=[2**k for k in range(2, 25)],
    logx=True,
    logy=True,
    equality_check=False, 
    xlabel='len(df)')

Couple of ways using count or sum

In [338]: df
Out[338]:
  col1 education
0    a       9th
1    b       9th
2    c       8th

In [335]: df.loc[df.education == '9th', 'education'].count()
Out[335]: 2

In [336]: (df.education == '9th').sum()
Out[336]: 2

In [337]: df.query('education == "9th"').education.count()
Out[337]: 2

An elegant way to count the occurrence of '?' or any symbol in any column, is to use built-in function isin of a dataframe object.

Suppose that we have loaded the 'Automobile' dataset into df object. We do not know which columns contain missing value ('?' symbol), so let do:

df.isin(['?']).sum(axis=0)

DataFrame.isin(values) official document says:

> it returns boolean DataFrame showing whether each element in the DataFrame > is contained in values

Note that isin accepts an iterable as input, thus we need to pass a list containing the target symbol to this function. df.isin(['?']) will return a boolean dataframe as follows.

	symboling	normalized-losses	make	fuel-type	aspiration-ratio ...
0	False	    True	            False	False	    False
1	False	    True	            False	False	    False
2	False	    True	            False	False	    False
3	False	    False	            False	False	    False
4	False	    False	            False	False	    False
5	False	    True	            False	False	    False
...

To count the number of occurrence of the target symbol in each column, let's take sum over all the rows of the above dataframe by indicating axis=0. The final (truncated) result shows what we expect:

symboling             0
normalized-losses    41
...
bore                  4
stroke                4
compression-ratio     0
horsepower            2
peak-rpm              2
city-mpg              0
highway-mpg           0
price                 4

Try this:

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

easy but not efficient:

list(df.education).count('9th')

for finding a specific value of a column you can use the code below

irrespective of the preference you can use the any of the method you like

df.col_name.value_counts().Value_you_are_looking_for

take example of the titanic dataset

df.Sex.value_counts().male

this gives a count of all male on the ship Although if you want to count a numerical data then you cannot use the above method because value_counts() is used only with series type of data hence fails So for that you can use the second method example

the second method is

#this is an example method of counting on a data frame
df[(df['Survived']==1)&(df['Sex']=='male')].counts()

this is not that efficient as value_counts() but surely will help if you want to count values of a data frame hope this helps

EDIT -- If you wanna look for something with a space in between

you may use

df.country.count('united states') I believe this should solve the problem

Simple example to count occurrences (unique values) in a column in Pandas data frame:

import pandas as pd

# URL to .csv file 
data_url = 'https://yoursite.com/Arrests.csv'
# Reading the data 
df = pd.read_csv(data_url, index_col=0)
# pandas count distinct values in column 
df['education'].value_counts()

Outputs:

Education        47516 
9th              41164 
8th              25510 
7th              25198 
6th              25047                       
...  
3rd                 2 
2nd                 2 
1st                 2 
Name: name, Length: 190, dtype: int64