Selecting Relevant Data for Plotting: A Case Study on Printing Only a Specific Column for Some Specific Stations from a CSV File

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In this article, we’ll delve into the process of selecting relevant data for plotting specific columns from a large CSV file. We’ll explore how to filter data based on station names and plot queue length per hour for top-performing stations.

Background

The problem at hand involves working with a large CSV file that contains charging simulation data. The goal is to select only the top three stations based on visited cars during the day, extract specific columns of interest, and then plot the queue length per hour for these stations.

Technical Details

The provided code uses Python libraries such as Pandas, Matplotlib, and Seaborn to manipulate and visualize data. Specifically:

pd.read_csv() is used to import the CSV file into a Pandas dataframe.
file_to_read.columns returns a list of column names in the dataframe.
file_to_read.describe() generates statistical descriptions for each column.
visited_cars_at_hour_24 creates a boolean mask to filter rows where the hour is 24.
filter() uses the mask to filter out irrelevant data from the dataframe.
top_three.nlargest(3, 'visited_cars') selects the top three stations based on visited cars during the day.

The Challenge

The original code attempts to plot queue length per hour for all hours using Matplotlib. However, this approach is inefficient and unnecessary. Instead, we want to focus on plotting only the specific columns of interest (queue length per hour) for the top three stations.

Solution Overview

Our solution involves:

Filtering the data to select only relevant rows based on station names.
Plotting queue length per hour for each of the top three stations using Seaborn’s factorplot() function.

Code Implementation

Step 1: Select Relevant Data

import pandas as pd

# Load the CSV file into a Pandas dataframe
file_to_read = pd.read_csv('results_per_hour/hotspot_districts_results_from_simulation.csv', sep=";", encoding='ISO-8859-1')

# Create a boolean mask to filter rows where the hour is 24
visited_cars_at_hour_24 = file_to_read['hour'] == 24

# Filter out irrelevant data from the dataframe using the mask
filtered = file_to_read.where(visited_cars_at_hour_24, inplace=False, axis=0)

# Select only the top three stations based on visited cars during the day
top_three = filtered.nlargest(3, 'visited_cars')

# Extract column names of interest (queue length per hour)
read_columns_of_file = file_to_read.columns
queue_length_column = [col for col in read_columns_of_file if 'cars_in_queue' in col]

Step 2: Plot Queue Length Per Hour

import seaborn as sns
import matplotlib.pyplot as plt

# Extract the top three stations
top_three_stations = top_three['name'].tolist()

# Filter data to include only relevant rows for each station
station_data = {}
for station in top_three_stations:
    filtered_station = top_three[~top_three['name'].isin(station)].nlargest(1, 'visited_cars')
    filtered_station = filtered_station[['hour', queue_length_column[0]]]
    station_data[station] = filtered_station

# Plot queue length per hour for each station using Seaborn's factorplot()
plt.figure(figsize=(12, 6))
for i, (station, data) in enumerate(station_data.items()):
    sns.factorplot(x='hour', y=queue_length_column[0], data=data, hue='name')
    plt.title(f'Queue Length per Hour for {station}')
    plt.show()

# Print the plot title and station names
print("Plotting Queue Length Per Hour for Top Three Stations:")
for i, (station, _) in enumerate(station_data.items()):
    print(f"Station {i+1}: {station}")

Conclusion

By following this step-by-step guide, you can efficiently select relevant data from a large CSV file and plot queue length per hour for top-performing stations using Seaborn’s factorplot(). This approach ensures that only the most important columns of interest are extracted and visualized, resulting in a more focused and informative plot.

Additional Tips

Make sure to clean and preprocess your data before performing any analysis or visualization.
Use descriptive variable names and column labels for clarity and readability.
Experiment with different visualization tools and techniques to find the most suitable approach for your specific use case.

Last modified on 2024-11-11