Unlocking the Ride: Analyzing Uber Trips in NYC for Insights on the Busiest Hours and Days!

Uber has become a significant mode of transportation for individuals residing in urban regions. There are individuals who do not possess their own vehicles, while others intentionally choose not to drive due to their hectic schedules. As a result, a variety of individuals are utilizing the services of Uber and similar taxi companies. This article will guide you through the analysis of Uber trips using Python.

Through the analysis of Uber trips, we can identify various trends such as peak and off-peak days or hours, as well as other patterns. The dataset utilized for this analysis pertains to Uber trips in New York, a city with a highly intricate transportation system and a large population.

This dataset consists of data regarding approximately 4.5 million Uber pickups in New York City between April and September, as well as 14.3 million pickups from January to June 2015. In addition to analysis, there are numerous other applications for this dataset. However, in the subsequent section, I will demonstrate how to conduct Uber Trips analysis utilizing Python.

Uber Trips Analysis using Python

I will start this task of Uber trips analysis by importing the necessary Python libraries and the dataset:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv("your_path/uber.csv")
data["Date/Time"] = data["Date/Time"].map(pd.to_datetime) 
data.head()

The dataset consists of information related to the date and time of the Uber pickup, latitude and longitude coordinates, as well as a “Base” column containing codes associated with the specific Uber pickup location. Additional datasets for conducting Uber trips analysis can be obtained from this source. For the purpose of analyzing Uber trips based on days and hours, let us now prepare the data being utilized for this analysis.

data["Day"] = data["Date/Time"].apply(lambda x: x.day)
data["Weekday"] = data["Date/Time"].apply(lambda x: x.weekday())
data["Hour"] = data["Date/Time"].apply(lambda x: x.hour)

To analyze the Uber trips for the month of September, we can examine the data on a day-by-day basis to determine which day had the highest number of trips. This information can be obtained by aggregating the data by day and then counting the number of trips for each day. We can then sort the days in descending order based on the number of trips to identify the day with the highest number of Uber trips.

sns.set(rc={'figure.figsize':(12, 10)})
sns.distplot(data["Day"])

To analyze the Uber trips according to hours, we can group the data by hour and then count the number of trips for each hour of the day. This will enable us to identify the busiest hours for Uber trips. We can then visualize the results using a graph to get a better understanding of the patterns in the data.

sns.distplot(data["Hour"])

To analyze the Uber trips based on weekdays, we can group the data by day of the week and then count the number of trips for each day. This will enable us to identify the days with the highest and lowest number of Uber trips. We can then visualize the results using a graph to get a better understanding of the patterns in the data.

sns.distplot(data["Weekday"])

To examine the correlation between hours and weekdays on Uber trips, we can create a heatmap to visualize the data. The heatmap will display the busiest hours for each day of the week, allowing us to identify any patterns or trends in the data.

# Correlation of Weekday and Hour
df = data.groupby(["Weekday", "Hour"]).apply(lambda x: len(x))
df = df.unstack()
sns.heatmap(df, annot=False)

As we are having the data about longitude and latitude so we can also plot the density of Uber trips according to the regions of the New Your city:

data.plot(kind='scatter', x='Lon', y='Lat', alpha=0.4, s=data['Day'], label='Uber Trips',
figsize=(12, 8), cmap=plt.get_cmap('jet'))
plt.title("Uber Trips Analysis")
plt.legend()
plt.show()

Summary

we can conclude that there is a clear correlation between the day of the week and the number of Uber trips, with higher demand on weekdays compared to weekends. Additionally, we can see that the busiest hours for Uber trips are during the late afternoon and early evening, indicating that people may be using Uber for commuting purposes during rush hour. Finally, the fact that most Uber trips originate near the Manhattan region in New York suggests that this area has a high demand for transportation services. Overall, these insights can be useful for Uber in terms of optimizing its operations and services to better meet the needs of its customers in New York City.