my code is this:
plt.pie(df['Rainfall'].value_counts().values,
labels = df['Rainfall'].value_counts().index,
autopct='%1.1f%%')
plt.show()
df.groupby('Rainfall').mean()
The issue I am having is that I am trying to show the probability of it raining however its giving many different values. All the values that are above 0 should be a yes that it will rain and all below should say it wont.
I am not sure how to do this and to separate the two
im following this guide: https://www.geeksforgeeks.org/rainfall-prediction-using-machine-learning-python/
The pie chart is attached below
I hope you can help!
Tried to follow the guide and read the documentation but am completely lost
I advice you to use the .apply()
method and a lambda function in order to easily classify between positive and negative values.
For example:
import pandas as pd
import matplotlib.pyplot as plt
data = {"location": [1, 1, 4, 3, 2], "Rainfall": [0, 0, 0.1, 0.04, 0.0001]}
df = pd.DataFrame(data)
df["Rainfall"] = df["Rainfall"].apply(lambda x: "YES" if x > 0 else "NO")
plt.pie(df["Rainfall"].value_counts().values, labels=df["Rainfall"].value_counts().index, autopct="%1.1f%%")
plt.show()
will output this