In [1]:

import pandas as pd
df = pd.read_csv('seattle_pet_licenses.csv')

In [4]:

df

	animal_s_name	license_issue_date	license_number	primary_breed	secondary_breed	species	zip_code
0	Ozzy	2005-03-29T00:00:00.000	130651.0	Dachshund, Standard Smooth Haired	NaN	Dog	98104
1	Jack	2009-12-23T00:00:00.000	898148.0	Schnauzer, Miniature	Terrier, Rat	Dog	98107
2	Ginger	2006-01-20T00:00:00.000	29654.0	Retriever, Golden	Retriever, Labrador	Dog	98117
3	Pepper	2006-02-07T00:00:00.000	75432.0	Manx	Mix	Cat	98103
4	Addy	2006-08-04T00:00:00.000	729899.0	Retriever, Golden	NaN	Dog	98105
...	...	...	...	...	...	...	...
66037	Lily	2016-12-27T00:00:00.000	NaN	Domestic Shorthair	Mix	Cat	98117
66038	Ellie	2016-11-29T00:00:00.000	NaN	German Shepherd	Mix	Dog	98105
66039	Sammy	2016-12-05T00:00:00.000	NaN	Terrier	Maltese	Dog	98105
66040	Buddy	2016-12-06T00:00:00.000	NaN	Bullmastiff	Mix	Dog	98105
66041	Aku	2016-12-07T00:00:00.000	NaN	Chihuahua, Short Coat	Terrier	Dog	98106

66042 rows × 7 columns

In [5]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 66042 entries, 0 to 66041
Data columns (total 7 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   animal_s_name       64685 non-null  object 
 1   license_issue_date  66042 non-null  object 
 2   license_number      43885 non-null  float64
 3   primary_breed       66042 non-null  object 
 4   secondary_breed     22538 non-null  object 
 5   species             66042 non-null  object 
 6   zip_code            65884 non-null  object 
dtypes: float64(1), object(6)
memory usage: 3.5+ MB

In [6]:

df['animal_s_name'].value_counts()

animal_s_name
Lucy          566
Bella         451
Charlie       447
Max           374
Luna          361
             ... 
Manasseh        1
Taba            1
Miriam          1
Number Six      1
Rollins         1
Name: count, Length: 15795, dtype: int64