housing_data

loadin housing data from scikit learn

Thinking

Loading and inspecting dataset

I need to provide an iterative answer, starting with loading and inspecting the dataset first. Since the user mentioned loading housing data from scikit-learn, it’s likely the California housing dataset due to the removal of Boston. I should show the shape, columns, and head() in sequence. To do this, I might create a DataFrame and display results using display(pd.Series(df.columns, name="column")) for columns, followed by df.head() as the final expression. I'll keep it concise and straightforward!

Let’s start by loading the California housing dataset from scikit-learn into a DataFrame and doing the basic first inspection.

</>Load housing data

import pandas as pd
from sklearn.datasets import fetch_california_housing
from IPython.display import display

data = fetch_california_housing(as_frame=True)
df = data.frame.copy()

df.shape
display(pd.Series(df.columns, name="column"))
df.head()

0         MedInc
1       HouseAge
2       AveRooms
3      AveBedrms
4     Population
5       AveOccup
6       Latitude
7      Longitude
8    MedHouseVal
Name: column, dtype: str

	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude	MedHouseVal
0	8.3252	41.0	6.984127	1.023810	322.0	2.555556	37.88	-122.23	4.526
1	8.3014	21.0	6.238137	0.971880	2401.0	2.109842	37.86	-122.22	3.585
2	7.2574	52.0	8.288136	1.073446	496.0	2.802260	37.85	-122.24	3.521
3	5.6431	52.0	5.817352	1.073059	558.0	2.547945	37.85	-122.25	3.413
4	3.8462	52.0	6.281853	1.081081	565.0	2.181467	37.85	-122.25	3.422

The housing data is already in the notebook as df, and the output confirms it loaded correctly from scikit-learn.

A few concrete details from what’s visible:
- The dataset has 9 columns.
- The target is MedHouseVal, which is included alongside the features.
- The feature columns are:
MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude
- The first rows show real numeric values for all columns, so it’s ready for analysis or modeling.

Right now df is a pandas DataFrame containing the California housing dataset from scikit-learn.

plot house value distribution

Thinking

Analyzing plotting needs

I need to provide an answer step-by-step, focusing on the latest outputs. The user requested a plot distribution, likely using seaborn or matplotlib defaults. It seems important to create just one chart. I’m thinking I’ll use the dataframe (df) and the target variable, MedHouseVal, mentioning that the values are numeric and the target exists. I should aim for concise code for a histogram with KDE without making assumptions beyond what I have.

MedHouseVal is the target, so the next useful step is a simple distribution plot.

</>Plot median house values

import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(8, 5))
sns.histplot(data=df, x="MedHouseVal", bins=40, kde=True)
plt.title("Distribution of Median House Value")
plt.xlabel("Median House Value")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

Thinking

Finalizing response strategy

I need to respond to the latest outputs and rules, deciding between two paths. The most recent output is a chart-only figure object, while earlier outputs include head values and columns. Following the rules, if the last output lacks numerical values, I should go with Path B to print the values used in the chart. It's essential to provide insights from the outputs along with a minimal runnable code block to gather the missing evidence focused on plotting the house value distribution. I’ll summarize the MedHouseVal and aim for a compact summary.Creating concise code block

I need one minimal code block without headings. I'll say something like: "You already have the distribution plot generated. The earlier data shows that MedHouseVal is numeric, with initial values between 3.4 and 4.5, making it suitable for plotting. To interpret the distribution beyond the figure, here’s a compact numeric summary:"

Then I’ll provide the following code:

summary = df["MedHouseVal"].describe(percentiles=[0.1,0.25,0.5,0.75,0.9]).to_frame("MedHouseVal")
summary

I won’t ask the user to run it or share any additional instructions, just presenting the code.Clarifying plot alignment

I can say something like, "The plotted column is the housing target, and it's clearly numeric in the notebook state, so the chart aligns well with what was requested. The most recent output confirms that a figure was created; however, it doesn't provide the specific shape of the distribution in text. That's a good observation!" This way, I'm ensuring that everything is clear and meaningful for the user.

The plot step is pointed at the right column: MedHouseVal is the numeric house value field, and the sample values shown in the notebook are all positive continuous values, so a histogram is the appropriate distribution plot.

The only missing piece is numeric evidence to interpret that distribution from the notebook output, since the latest output only shows that a figure object was created. A compact summary of MedHouseVal will make the plotted distribution interpretable.

</>Summarize MedHouseVal stats

df["MedHouseVal"].describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).to_frame("MedHouseVal")

	MedHouseVal
count	20640.000000
mean	2.068558
std	1.153956
min	0.149990
10%	0.823000
25%	1.196000
50%	1.797000
75%	2.647250
90%	3.766000
max	5.000010

The house value distribution is now well supported by both the plot and the summary values.

From the observed outputs:

MedHouseVal ranges from about 0.15 to 5.00.
The median is 1.797, while the mean is higher at 2.069, which suggests a right-skewed distribution.
Half of the homes fall roughly between 1.196 and 2.647 based on the 25th and 75th percentiles.
The 90th percentile is 3.766, while the maximum is 5.000, indicating a concentration of values below the upper end with a tail extending to high values.
The exact max being near 5.0 also suggests an upper cap in this target variable, which often appears as a pile-up near the high end in the histogram.

So the distribution you plotted is best interpreted as a unimodal distribution with noticeable right skew and a capped upper boundary around 5.0.