How to Iterate over rows in pandas, and why you shouldn't

how_to_loop_using_pandas.py

# %%
import httpx
import pandas as pd

# %% Read CSV and rename headers
websites = pd.read_csv("resources/popular_websites.csv", index_col=0)
print(websites)


# %% Define function to check connection
def check_connection(name, url):
    try:
        response = httpx.get(url)
        location = response.headers.get("location")
        if location is None or location.startswith(url):
            print(f"{name} is online!")
        else:
            print(f"{name} is online! But redirects to {location}")
        return True
    except httpx.ConnectError:
        print(f"Failed to establish a connection with {url}")
        return False


# %% Use .itertuples() to iterate through all rows
for website in websites.itertuples():
    check_connection(website.name, website.url)

# %% You may use .iterrows() if you have dynamic columnnames
name_column = "name"
url_column = "url"
for _, website in websites.iterrows():
    check_connection(website[name_column], website[url_column])

# %% Use list comprehension to iterate through all rows
#    Note that this creates a list that is thrown away again
[
    check_connection(website.name, website.url)
    for website in websites.itertuples()
]

# %% Use the index to iterate through rows
for i in websites.index:
    print({**websites.iloc[i]})

# %% Transpose and cast to dictionary to iterate through rows
for website in websites.T.to_dict().values():
    print(website)

# %% Use .agg() to aggregate over columns
websites.agg(
    total_views=("total_views", "sum"),
    average_views=("total_views", "mean"),
)

#!/usr/bin/env python3

# %%
import httpx
import pandas as pd

# %% Define function to check connection
def check_connection(name, url) -> bool :
    try:
        response = httpx.get(url)
        location = response.headers.get("location")
        if location is None or location.startswith(url):
            print(f"{name} is online!")
        else:
            print(f"{name} is online! But redirects to {location}")
        return True
    except httpx.ConnectError:
        print(f"Failed to establish a connection with {url}")
        return False

def my_main():
    print('\n**** Read CSV')
    # %% Read CSV and rename headers
    websites = pd.read_csv("resources/popular_websites.csv", index_col=0)
    print(websites)
    # %% Use .itertuples() to iterate through all rows
    print('\n**** Use .itertuples() method to iterate through all rows')
    for website in websites.itertuples():
        check_connection(website.name, website.url)

    # %% You may use .iterrows() if you have dynamic columnnames
    print('\n**** You may use .iterrows() if you have dynamic columnnames')
    name_column = "name"
    url_column = "url"
    for _, website in websites.iterrows():
        check_connection(website[name_column], website[url_column])

    print('\n**** Use list comprehension to iterate through all rows')
    # %% Use list comprehension to iterate through all rows
    #    Note that this creates a list that is thrown away again
    _ = [
        check_connection(website.name, website.url)
        for website in websites.itertuples()
    ]

    print('\n**** Use the index to iterate through rows')
    # %% Use the index to iterate through rows
    for i in websites.index:
        print({**websites.iloc[i]})

    print('\n**** Transpose and cast to dictionary to iterate through rows')
    # %% Transpose and cast to dictionary to iterate through rows
    for website in websites.T.to_dict().values():
        print(website)

    print('\n**** Use .agg() to aggregate over columns')
    # %% Use .agg() to aggregate over columns
    websites.agg(
        total_views=("total_views", "sum"),
        average_views=("total_views", "mean"),
    )
    print(websites)

if __name__ == "__main__":
    print('\n**** Begin test')
    my_main()