Animal Center

Exploratory Data Analysis with Python

Going through kaggle.com, I came across this dataset for animal shelter in Austin, and I found it useful to practice my Python skills

The following were covered:

• Grouping and Sorting
• Data Types and missing values
• Renaming and Combining

Import the Libraries

Before tackling a project, you must first understand what you want to achieve and how to achieve it. You have to import the important library you will need. For this dataset, i imported

Numpy

Matplotlib

Seaborn

Datetime (as we have a column "DATETIME")

• Set the parameters for the visulaizations like the size, color, and the style of plot to use

Import and Load the Data

We are working with a Comma Seperated Values file (CSV). To import into the notebook, we use

pd.csv('name_of_file.csv') and assign it to a variable named df, then run df

Going through the dataframe, we can see that some rows are empty, DATETIME and MONTHYEAR columns are the same, a name has an unwanted character.

Data Cleaning

Run

df.insna().sum() 

To know the number of empty cells in the dataframe. Since the empty rows are just in columns NAME and SEX_UPON_INTAKE, we fill them with Unknown, then make sure it effects the dataframe

df.fillna('Unknown', inplace=True)

To delete irrelevant columns, we use the drop method

col = ['MONTHYEAR', 'ANIMAL ID']

df.drop(col, axis = 1, inplace=True)

Convert DATETIME to pandas datetime

df['DATETIME'] = pd.to_datetime(df['DateTime'], format= '%m/%d/%Y %I:%M:%S %p')

Extract words into a new column. I wasn't satisfied with the SEX_UPON_INTAKE column, as i only needed to know if they were male or female

df['New_Sex'] = df['Sex upon Intake'].str.extract(r'(Male|Female|Unknown)')

Find the total number of animals brought in each year.

df['Year'] = df['DateTime'].dt.year #extract only the year into a new column

df.groupby(['Year','Animal Type']).size()

I also converted the Age_upon_Intake column to weeks and extracted into a new column, as this would be useful for visualizations. Any value above 200 means the animal is 3 years or older.

With these, I could visualize the following for Insights:

• Total number of animals in the shlter
• Animals brought in by their conditions (normal, sick, injured, etc)
• Percentage of the types of animals taken in (stray, wildlifee, abandoned, etc)
• Age upon Intake (in weeks)
• Sex of animals (male, female)
• Total number of animals brought in yearly