Python Automation for Group by and Line Chart

 def conversion_rate(dataframe, column_names):

    # Total number of converted users
    column_conv = dataframe[dataframe['converted'] == True]\
                       .groupby(column_names)['user_id'].nunique()

    # Total number users
    column_total = dataframe.groupby(column_names)['user_id'].nunique()   
    
    # Conversion rate 
    conversion_rate = column_conv/column_total
    
    # Fill missing values with 0
    conversion_rate = conversion_rate.fillna(0)
    return conversion_rate
Testing the function
# Calculate conversion rate by age_group
age_group_conv = conversion_rate(marketing, ['date_served', 'age_group'])
print(age_group_conv)

# Unstack and create a DataFrame
age_group_df = pd.DataFrame(age_group_conv.unstack(level=1))

# Visualize conversion by age_group
age_group_df.plot()
plt.title('Conversion rate by age group\n', size = 16)
plt.ylabel('Conversion rate', size = 14)
plt.xlabel('Age group', size = 14)
plt.show()

Instructions
100 XP
  • Create a for loop for each column in the dataframe.
  • Plot a line chart of the column by the DataFrame's index.
  • Show the plot.
def plotting_conv(dataframe):
    for column in dataframe:
        # Plot column by dataframe's index
        plt.plot(dataframe.index,dataframe[column])
        plt.title('Daily ' + str(column) + ' conversion rate\n', 
                  size = 16)
        plt.ylabel('Conversion rate', size = 14)
        plt.xlabel('Date', size = 14)
        # Show plot
        plt.show() 
        plt.clf()
Testing the function
# Calculate conversion rate by date served and age group
age_group_conv = conversion_rate(marketing,['date_served','age_group'])

# Unstack age_group_conv and create a DataFrame
age_group_df = pd.DataFrame(age_group_conv.unstack(level=1))

# Plot the results
plotting_conv(age_group_df)

Comments

Post a Comment

Popular posts from this blog

Binomial Test in Python

  Binomial Test Let’s imagine that we are analyzing the percentage of customers who make a purchase after visiting a website. 1000 customers visited the site this month, and 58 of them made a purchase. The marketing department reports that historical data suggests about 72 of every 1000 visitors make a purchase. Thus, they estimate that the probability of any particular customer making a purchase is 7.2%. We would like to know if this month’s number, 58 purchases, is significantly different from normal or a reasonable fluctuation due to random chance. In previous exercises, we collected samples of numerical information (eg. order price) and then used the mean and standard deviation of those samples to make comparisons. In contrast, we now have a sample where each unit (a visitor) falls into one of two discrete categories: “made a purchase” “did not make a purchase” Instead of comparing sample means, we want to compare the percent in the “made a purchase” category to some expectatio...
Read more

Python Syntax and Functions Part2 (Summary Statistics)

The  .agg()  method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super efficient. In the custom function for this exercise, "IQR" is short for inter-quartile range, which is the 75th percentile minus the 25th percentile. It's an alternative to standard deviation that is helpful if your data contains   import numpy as np def iqr(column):     return column.quantile(0.75) - column.quantile(0.25) # Update to print IQR and median of temperature_c, fuel_price_usd_per_l, & unemployment print(sales[["temperature_c", "fuel_price_usd_per_l", "unemployment"]].agg([iqr, np.median])) Calulating Cumulative Statistics Syntax = df.drop_duplicates sales_1_1 = sales_1_1.sort_values("date") # Get the cumulative sum of weekly_sales, add as cum_weekly_sales col sales_1_1["cum_weekly_sales"] = sales_1_1["weekly_sales...
Read more

Slicing and Indexing in Python Pandas

 # Make a list of cities to subset on cities = ["Moscow", "Saint Petersburg"] # Subset temperatures using square brackets print(temperatures[temperatures["city"].isin(cities)]) # Subset temperatures_ind using .loc[] print(temperatures_ind.loc[cities]) temperatures_ind = temperatures.set_index(['country','city']) # List of tuples: Brazil, Rio De Janeiro & Pakistan, Lahore rows_to_keep = [('Brazil','Rio De Janeiro'),('Pakistan','Lahore')] # Subset for rows to keep print(temperatures_ind.loc[rows_to_keep]) # Sort temperatures_ind by index values print(temperatures_ind.sort_index()) # Sort temperatures_ind by index values at the city level print(temperatures_ind.sort_index(level="city")) # Sort temperatures_ind by country then descending city print(temperatures_ind.sort_index(level=["country", "city"], ascending = [True, False])) Slice rows with code like  df.loc[("a", ...
Read more