WEEK 03 - Probability¶
Goal¶
Probability distribution of your data¶
In [5]:
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import norm
df = pd.read_csv("datasets/MotorVehicle_CrashRecord.csv")
df.head()
df["CRASH_HOUR"] = pd.to_datetime(df["CRASH TIME"], errors="coerce").dt.hour
df["CRASH_HOUR"].dropna().head()
plt.figure(figsize=(8, 5))
df["CRASH_HOUR"].plot(
kind="hist",
bins=24,
density=True
)
plt.xlabel("Hour of Day")
plt.ylabel("Probability Density")
plt.title("Probability Distribution of Motor Vehicle Crashes by Hour")
plt.show()
injuries = df["NUMBER OF PERSONS INJURED"].dropna()
plt.figure(figsize=(8, 5))
plt.hist(injuries, bins=30, density=True)
plt.xlabel("Number of Persons Injured")
plt.ylabel("Probability Density")
plt.title("Probability Distribution of Number of Persons Injured")
plt.show()
mean = injuries.mean()
std = injuries.std()
x = injuries.sort_values()
plt.figure(figsize=(8, 5))
plt.hist(injuries, bins=30, density=True, alpha=0.6)
plt.plot(x, norm.pdf(x, mean, std))
plt.xlabel("Number of Persons Injured")
plt.ylabel("Probability Density")
plt.title("Injury Distribution vs Normal Distribution")
plt.show()
injuries.describe()
/tmp/ipykernel_477/2859588920.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. df["CRASH_HOUR"] = pd.to_datetime(df["CRASH TIME"], errors="coerce").dt.hour
Out[5]:
count 200.000000 mean 0.515000 std 0.789233 min 0.000000 25% 0.000000 50% 0.000000 75% 1.000000 max 4.000000 Name: NUMBER OF PERSONS INJURED, dtype: float64
Template notebook and slides¶
TASK: Create a notebook with the analysis of our data set (Currently need to make a dummy notebook for future work), store it in your repo, and call it presentation.ipynb
In [ ]: