Python Writing an Iterator and a Function to add columns automation

 # Define plot_pop()

def plot_pop(filename, country_code):


    # Initialize reader object: urb_pop_reader

    urb_pop_reader = pd.read_csv(filename, chunksize=1000)


    # Initialize empty DataFrame: data

    data = pd.DataFrame()

    

    # Iterate over each DataFrame chunk

    for df_urb_pop in urb_pop_reader:

        # Check out specific country: df_pop_ceb

        df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode'] == country_code]


        # Zip DataFrame columns of interest: pops

        pops = zip(df_pop_ceb['Total Population'],

                    df_pop_ceb['Urban population (% of total)'])


        # Turn zip object into list: pops_list

        pops_list = list(pops)


        # Use list comprehension to create new DataFrame column 'Total Urban Population'

        df_pop_ceb['Total Urban Population'] = [int(tup[0] * tup[1] * 0.01) for tup in pops_list]

    

        # Append DataFrame chunk to data: data

        data = data.append(df_pop_ceb)


    # Plot urban population data

    data.plot(kind='scatter', x='Year', y='Total Urban Population')

    plt.show()


# Set the filename: fn

fn = 'ind_pop_data.csv'


# Call plot_pop for country code 'CEB'

plot_pop('ind_pop_data.csv','CEB')


# Call plot_pop for country code 'ARB'

plot_pop('ind_pop_data.csv','ARB')


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