import pandas as pd

data_link = r'http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'

If you look at the dataset, it has few missing values that have been represented by '?'. We need to replace '?' with nan(not a number) so that Python recognises them as null/missing values, while reading the data in Python.

We will use read_csv function of pandas to read the data in Python from web.

• ',\s' is being used as separator which means a comma and a single white space
• na_values parameter has been passed value as ["?"] to tell it to consider '?' as null

adult_data = pd.read_csv(data_link, header=None, sep=',\s', na_values=["?"])

C:\Users\crackthetech\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
  """Entry point for launching an IPython kernel.

Ignore the warning as it just says that, to read data, 'c' engine is not being used because it does not support regex(sep=',\s'). Rather, python engine is being used for the purpose.

Let's have a look at top 5 rows of data

adult_data.head()

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14
0	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	0	40	United-States	<=50K
1	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	13	United-States	<=50K
2	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	0	40	United-States	<=50K
3	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	40	United-States	<=50K
4	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	0	40	Cuba	<=50K

Digits are being used as column names as of now. We need to provide the dataframe column names. Column names are given in the link of dataset. Take them in a list as below.

column_names = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship',
                'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'salary']

Assign column names to dataframe

adult_data.columns = column_names

Now, let's have a look at data type of column

adult_data.dtypes

age                int64
workclass         object
fnlwgt             int64
education         object
education-num      int64
marital-status    object
occupation        object
relationship      object
race              object
sex               object
capital-gain       int64
capital-loss       int64
hours-per-week     int64
native-country    object
salary            object
dtype: object

adult_data.head(5)

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	capital-loss	hours-per-week	native-country	salary
0	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	0	40	United-States	<=50K
1	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	13	United-States	<=50K
2	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	0	40	United-States	<=50K
3	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	40	United-States	<=50K
4	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	0	40	Cuba	<=50K

Just check the shape of dataframe

adult_data.shape

(32561, 15)

adult_data = adult_data.iloc[:1000]  # for simplicity taking only first 1000 rows

adult_data.shape

(1000, 15)