Histogram

Contents

Histogram#

Visualize the distribution of a single variable with a histogram.

Goal

Visualize the distribution of indoor temperature for Flat A. A histogram counts how many hours fall into each temperature bin and reveals whether temperatures are symmetric, skewed, or have several peaks (e.g. heating season and summer).

Data Basis

Dataset: flatTempHum.csv — Hourly indoor temperature and humidity measurements.
Column used: FlatA_Temp
Time range: October 2018 – September 2020

Data Preparation#

import pandas as pd

url = "https://raw.githubusercontent.com/retomarek/edap/main/edap/sampleData/flatTempHum.csv"
df = pd.read_csv(url, sep=";")
df["time"] = pd.to_datetime(df["time"])
df = df.set_index("time", drop=True)
df.head()

	FlatA_Hum	FlatA_Temp	FlatB_Hum	FlatB_Temp	FlatC_Hum	FlatC_Temp	FlatD_Hum	FlatD_Temp
time
2018-10-03 00:00:00	53.0	24.43	38.8	22.40	44.0	24.5	49.0	24.43
2018-10-03 01:00:00	53.0	24.40	38.8	22.40	44.0	24.5	49.0	24.40
2018-10-03 02:00:00	53.0	24.40	39.3	22.40	44.7	24.5	48.3	24.38
2018-10-03 03:00:00	53.0	24.40	40.3	22.40	45.0	24.5	48.0	24.33
2018-10-03 04:00:00	53.3	24.40	41.0	22.37	45.2	24.5	47.7	24.30

Visualization#

from pyedautils.plots import plot_distribution

fig = plot_distribution(
    df,
    columns=["FlatA_Temp"],
    nbins=50,
    title="Temperature Distribution — Flat A",
    xlab="Temperature [°C]",
)
fig.show()