Using descriptive statistics#

Statistics describe important aspects of our data, often revealing deeper insights.

Statistics is a branch of mathematics concerned with data collection, analysis, interpretation, presentation, and organization.

It plays a crucial role in various fields, from business and economics to healthcare and social sciences. Using statistical techniques, we can describe essential aspects of our data and uncover patterns and trends that may not be immediately apparent. Statistics can help us make informed decisions, identify potential problems, and evaluate the effectiveness of interventions.

In short, statistics can reveal more profound insights into our data and provide valuable information that can guide us in making better decisions.

How To#

import pandas as pd
df = pd.read_csv("data/housing.csv")
df.head()
longitude latitude housing_median_age total_rooms total_bedrooms population households median_income median_house_value ocean_proximity
0 -122.23 37.88 41.0 880.0 129.0 322.0 126.0 8.3252 452600.0 NEAR BAY
1 -122.22 37.86 21.0 7099.0 1106.0 2401.0 1138.0 8.3014 358500.0 NEAR BAY
2 -122.24 37.85 52.0 1467.0 190.0 496.0 177.0 7.2574 352100.0 NEAR BAY
3 -122.25 37.85 52.0 1274.0 235.0 558.0 219.0 5.6431 341300.0 NEAR BAY
4 -122.25 37.85 52.0 1627.0 280.0 565.0 259.0 3.8462 342200.0 NEAR BAY 
df.describe()
longitude latitude housing_median_age total_rooms total_bedrooms population households median_income median_house_value
count 20640.000000 20640.000000 20640.000000 20640.000000 20433.000000 20640.000000 20640.000000 20640.000000 20640.000000
mean -119.569704 35.631861 28.639486 2635.763081 537.870553 1425.476744 499.539680 3.870671 206855.816909
std 2.003532 2.135952 12.585558 2181.615252 421.385070 1132.462122 382.329753 1.899822 115395.615874
min -124.350000 32.540000 1.000000 2.000000 1.000000 3.000000 1.000000 0.499900 14999.000000
25% -121.800000 33.930000 18.000000 1447.750000 296.000000 787.000000 280.000000 2.563400 119600.000000
50% -118.490000 34.260000 29.000000 2127.000000 435.000000 1166.000000 409.000000 3.534800 179700.000000
75% -118.010000 37.710000 37.000000 3148.000000 647.000000 1725.000000 605.000000 4.743250 264725.000000
max -114.310000 41.950000 52.000000 39320.000000 6445.000000 35682.000000 6082.000000 15.000100 500001.000000 
df.groupby("ocean_proximity").median()
longitude latitude housing_median_age total_rooms total_bedrooms population households median_income median_house_value
ocean_proximity
<1H OCEAN -118.275 34.03 30.0 2108.0 438.0 1247.0 421.0 3.87500 214850.0
INLAND -120.000 36.97 23.0 2131.0 423.0 1124.0 385.0 2.98770 108500.0
ISLAND -118.320 33.34 52.0 1675.0 512.0 733.0 288.0 2.73610 414700.0
NEAR BAY -122.250 37.79 39.0 2083.0 423.0 1033.5 406.0 3.81865 233800.0
NEAR OCEAN -118.260 33.79 29.0 2195.0 464.0 1136.5 429.0 3.64705 229450.0 
df.agg({"longitude": ["min", "max", "mean"],
        "latitude": ["min", "max", "mean"],
        "total_rooms": ["min", "max", "median"],
        "median_income": ["skew"]})
longitude latitude total_rooms median_income
max -114.310000 41.950000 39320.0 NaN
mean -119.569704 35.631861 NaN NaN
median NaN NaN 2127.0 NaN
min -124.350000 32.540000 2.0 NaN
skew NaN NaN NaN 1.646657 
df["ocean_proximity"].value_counts()

<1H OCEAN     9136
INLAND        6551
NEAR OCEAN    2658
NEAR BAY      2290
ISLAND           5
Name: ocean_proximity, dtype: int64

df.corr('spearman')
longitude latitude housing_median_age total_rooms total_bedrooms population households median_income median_house_value
longitude 1.000000 -0.879203 -0.150752 0.040120 0.063879 0.123527 0.060020 -0.009928 -0.069667
latitude -0.879203 1.000000 0.032440 -0.018435 -0.056636 -0.123626 -0.074299 -0.088029 -0.165739
housing_median_age -0.150752 0.032440 1.000000 -0.357162 -0.306544 -0.283879 -0.281989 -0.147308 0.074855
total_rooms 0.040120 -0.018435 -0.357162 1.000000 0.915021 0.816185 0.906734 0.271321 0.205952
total_bedrooms 0.063879 -0.056636 -0.306544 0.915021 1.000000 0.870937 0.975627 -0.006196 0.086259
population 0.123527 -0.123626 -0.283879 0.816185 0.870937 1.000000 0.903872 0.006268 0.003839
households 0.060020 -0.074299 -0.281989 0.906734 0.975627 0.903872 1.000000 0.030305 0.112737
median_income -0.009928 -0.088029 -0.147308 0.271321 -0.006196 0.006268 0.030305 1.000000 0.676778
median_house_value -0.069667 -0.165739 0.074855 0.205952 0.086259 0.003839 0.112737 0.676778 1.000000

Exercise#

Additional Resources#