Skip to content
Hypothesis Testing in Python
  • AI Chat
  • Code
  • Report

    • Hypothesis Testing in Python

    Run the hidden code cell below to import the data used in this course.

    # Import pandas
import pandas as pd

# Import the course datasets 
republican_votes = pd.read_feather('datasets/repub_votes_potus_08_12.feather')
democrat_votes = pd.read_feather('datasets/dem_votes_potus_12_16.feather')
shipments = pd.read_feather('datasets/late_shipments.feather')
stackoverflow = pd.read_feather("datasets/stack_overflow.feather")

    Take Notes

    Add notes about the concepts you've learned and code cells with code you want to keep.

    Add your notes here

    # Add your code snippets here

    Calculating the sample mean

    In pandas, a value's proportion in a categorical DataFrame column can be quickly calculated using the syntax:

    prop = (df['col'] == val).mean()

    Calculating a z-score

    o valor-p é o menor nível de significância com que se rejeitaria a hipótese nula.

    P-VALUE

    In order to determine whether to choose the null hypothesis or the alternative hypothesis, you need to calculate a p-value from the z-score.

    # Calculate the z-score of late_prop_samp
z_score = (late_prop_samp - late_prop_hyp)/std_error

# Calculate the p-value
p_value = 1 - norm.cdf(z_score, loc=0, scale=1)
                 
# Print the p-value
print(p_value)

    ‌
    ‌
    ‌