In [1]:
import pandas as pd
import numpy as np
In [2]:
features = ["Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Martial Status", "Occupation", "Relationship", 
             "Race", "Sex", "Capital Gain", "Capital Loss", "Hours per week", "Country", "Target"] 

df = pd.read_csv('adult.data', names=features)
df
Out[2]:
Age Workclass fnlwgt Education Education-Num Martial Status Occupation Relationship Race Sex Capital Gain Capital Loss Hours per week Country Target
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
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
32556 27 Private 257302 Assoc-acdm 12 Married-civ-spouse Tech-support Wife White Female 0 0 38 United-States <=50K
32557 40 Private 154374 HS-grad 9 Married-civ-spouse Machine-op-inspct Husband White Male 0 0 40 United-States >50K
32558 58 Private 151910 HS-grad 9 Widowed Adm-clerical Unmarried White Female 0 0 40 United-States <=50K
32559 22 Private 201490 HS-grad 9 Never-married Adm-clerical Own-child White Male 0 0 20 United-States <=50K
32560 52 Self-emp-inc 287927 HS-grad 9 Married-civ-spouse Exec-managerial Wife White Female 15024 0 40 United-States >50K

32561 rows × 15 columns

https://rstudio-pubs-static.s3.amazonaws.com/538563_85cb2b4cd06b4dc48d33de73fa97a297.html

https://archive.ics.uci.edu/dataset/2/adult

1. How many men and women (sex feature) are represented in this dataset?¶

In [3]:
gender_counts = df['Sex'].value_counts()
In [4]:
print(gender_counts)

 Male      21790
 Female    10771
Name: Sex, dtype: int64

2. What is the average age (age feature) of women?¶

In [5]:
#women_data = df[(df['Sex'] == 'Female') & (df['Age'].notnull())]
age_avg = df['Age'].mean()
print(age_avg)
print(df['Age'].unique())
women_data = df[['Age','Sex']]
#women_data_age = df[['Age','Sex' == 'Female']]
#women_data_age
#df.pivot_table(df,index=['Sex'])
female_data = df[df['Sex'].str.contains('Female')]
women_age_avg = female_data['Age'].mean()
women_age_avg

38.58164675532078
[39 50 38 53 28 37 49 52 31 42 30 23 32 40 34 25 43 54 35 59 56 19 20 45
 22 48 21 24 57 44 41 29 18 47 46 36 79 27 67 33 76 17 55 61 70 64 71 68
 66 51 58 26 60 90 75 65 77 62 63 80 72 74 69 73 81 78 88 82 83 84 85 86
 87]
Out[5]:
36.85823043357163

3. What is the proportion of German citizens (native-country feature)?¶

In [14]:
German_citizen_data = df[df['Country'].str.contains('Germany')]
num_of_german = len(German_citizen_data)
#num_of_german
total_rows = len(df)
#total_rows
prportion_of_german_citizen = (num_of_german/total_rows)
prportion_of_german_citizen

object
Out[14]:
0.004207487485028101

4-5. What are mean value and standard deviation of the age of those who recieve more than 50K per year (salary feature) and those who receive less than 50K per year?¶

In [16]:
data_morethan_50 = df[df['Target'].str.contains('>50K')]
data_morethan_50
#df['Target'].unique()
Out[16]:
Age Workclass fnlwgt Education Education-Num Martial Status Occupation Relationship Race Sex Capital Gain Capital Loss Hours per week Country Target
7 52 Self-emp-not-inc 209642 HS-grad 9 Married-civ-spouse Exec-managerial Husband White Male 0 0 45 United-States >50K
8 31 Private 45781 Masters 14 Never-married Prof-specialty Not-in-family White Female 14084 0 50 United-States >50K
9 42 Private 159449 Bachelors 13 Married-civ-spouse Exec-managerial Husband White Male 5178 0 40 United-States >50K
10 37 Private 280464 Some-college 10 Married-civ-spouse Exec-managerial Husband Black Male 0 0 80 United-States >50K
11 30 State-gov 141297 Bachelors 13 Married-civ-spouse Prof-specialty Husband Asian-Pac-Islander Male 0 0 40 India >50K
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
32539 71 ? 287372 Doctorate 16 Married-civ-spouse ? Husband White Male 0 0 10 United-States >50K
32545 39 Local-gov 111499 Assoc-acdm 12 Married-civ-spouse Adm-clerical Wife White Female 0 0 20 United-States >50K
32554 53 Private 321865 Masters 14 Married-civ-spouse Exec-managerial Husband White Male 0 0 40 United-States >50K
32557 40 Private 154374 HS-grad 9 Married-civ-spouse Machine-op-inspct Husband White Male 0 0 40 United-States >50K
32560 52 Self-emp-inc 287927 HS-grad 9 Married-civ-spouse Exec-managerial Wife White Female 15024 0 40 United-States >50K

7841 rows × 15 columns

In [17]:
mean_age = data_morethan_50['Age'].mean()
mean_age
Out[17]:
44.24984058155847
In [18]:
standard_deviation = np.std(data_morethan_50['Age'])
standard_deviation
Out[18]:
10.518356927661575
In [19]:
data_lessthan_50 = df[df['Target'].str.contains('<=50K')]
data_lessthan_50
Out[19]:
Age Workclass fnlwgt Education Education-Num Martial Status Occupation Relationship Race Sex Capital Gain Capital Loss Hours per week Country Target
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
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
32553 32 Private 116138 Masters 14 Never-married Tech-support Not-in-family Asian-Pac-Islander Male 0 0 11 Taiwan <=50K
32555 22 Private 310152 Some-college 10 Never-married Protective-serv Not-in-family White Male 0 0 40 United-States <=50K
32556 27 Private 257302 Assoc-acdm 12 Married-civ-spouse Tech-support Wife White Female 0 0 38 United-States <=50K
32558 58 Private 151910 HS-grad 9 Widowed Adm-clerical Unmarried White Female 0 0 40 United-States <=50K
32559 22 Private 201490 HS-grad 9 Never-married Adm-clerical Own-child White Male 0 0 20 United-States <=50K

24720 rows × 15 columns

In [21]:
mean_age_lessthan = data_lessthan_50['Age'].mean()
mean_age_lessthan
Out[21]:
36.78373786407767
In [22]:
standard_deviation_lessthan = np.std(data_lessthan_50['Age'])
standard_deviation_lessthan
Out[22]:
14.019804910115214

6. Is it true that people who receive more than 50k have at least high school education? (education - Bachelors, Prof-school, Assoc-acdm, Assoc-voc, Masters or Doctorate feature)¶

In [23]:
required_education_levels = ['Bachelors', 'Prof-school', 'Assoc-acdm', 'Assoc-voc', 'Masters', 'Doctorate']
is_high_education = all(level in required_education_levels for level in data_morethan_50['Education'])
if is_high_education:
    print("People who receive more than 50K have at least a high school education.")
else:
    print("People who receive more than 50K do not necessarily have at least a high school education.")

People who receive more than 50K do not necessarily have at least a high school education.
In [ ]: