Key Word(s): Introduction, Data Science, EDA, Hubway
In [26]:
import pandas as pd
import sys
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from math import radians, cos, sin, asin, sqrt
import datetime
from sklearn.linear_model import LinearRegression
import seaborn as sns
sns.set(style="ticks")
%matplotlib inline
Download the data from
https://drive.google.com/open?id=0B28c493CP9GtMzN1emFoMkJNNlU
First Look At The Data¶
In [27]:
hubway_data = pd.read_csv('hubway_trips.csv', low_memory=False)
hubway_data.head()
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Who?¶
In [28]:
year_to_age = lambda s: 0 if 'N' in s else 2017 - int(s)
In [29]:
fig, ax = plt.subplots(1, 2, figsize=(15, 6))
gender_counts = np.unique(hubway_data['gender'].replace(np.nan, 'NaN', regex=True).values, return_counts=True)
ax[0].bar(range(3), gender_counts[1], align='center', color=['black', 'green', 'teal'], alpha=0.5)
ax[0].set_xticks([0, 1, 2])
ax[0].set_xticklabels(['none', 'male', 'female', ' '])
ax[0].set_title('Users by Gender')
age_col = 2017.0 - hubway_data['birth_date'].dropna().values
age_counts = np.unique(age_col, return_counts=True)
ax[1].bar(age_counts[0], age_counts[1], align='center', width=0.4, alpha=0.6)
ax[1].axvline(x=np.mean(age_col), color='red', label='average age')
ax[1].axvline(x=np.percentile(age_col, 25), color='red', linestyle='--', label='lower quartile')
ax[1].axvline(x=np.percentile(age_col, 75), color='red', linestyle='--', label='upper quartile')
ax[1].set_xlim([1, 90])
ax[1].set_xlabel('Age')
ax[1].set_ylabel('Number of Checkouts')
ax[1].legend()
ax[1].set_title('Users by Age')
plt.tight_layout()
plt.show()
Where¶
In [30]:
station_data = pd.read_csv('hubway_stations.csv', low_memory=False)[['id', 'lat', 'lng']]
station_data.head()
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In [31]:
hubway_data_with_gps = hubway_data.join(station_data.set_index('id'), on='strt_statn')
hubway_data_with_gps.head()
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When¶
In [32]:
#check_out_times = pd.to_datetime(hubway_data['start_date'])
check_out_hours = hubway_data['start_date'].apply(lambda s: int(s[-8:-6]))
In [33]:
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
check_out_counts = np.unique(check_out_hours, return_counts=True)
ax.bar(check_out_counts[0], check_out_counts[1], align='center', width=0.4, alpha=0.6)
ax.set_xlim([-1, 24])
ax.set_xticks(range(24))
ax.set_xlabel('Hour of Day')
ax.set_ylabel('Number of Checkouts')
ax.set_title('Time of Day vs Checkouts')
plt.show()
How¶
In [34]:
def haversine(pt, lat2=42.355589, lon2=-71.060175):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
lon1 = pt[0]
lat1 = pt[1]
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 3956 # Radius of earth in miles
return c * r
In [35]:
station_counts = np.unique(hubway_data_with_gps['strt_statn'].dropna(), return_counts=True)
counts_df = pd.DataFrame({'id':station_counts[0], 'checkouts':station_counts[1]})
counts_df = counts_df.join(station_data.set_index('id'), on='id')
counts_df.head()
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In [47]:
counts_df.loc[:, 'dist_to_center'] = list(map(haversine, counts_df[['lng', 'lat']].values))
counts_df.head()
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In [49]:
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
ax.scatter(counts_df['dist_to_center'].values, counts_df['checkouts'].values)
reg_line = LinearRegression()
reg_line.fit(counts_df['dist_to_center'].values.reshape((len(counts_df['dist_to_center']), 1)), counts_df['checkouts'].values)
distances = np.linspace(counts_df['dist_to_center'].min(), counts_df['dist_to_center'].max(), 50)
ax.plot(distances, reg_line.predict(distances.reshape((len(distances), 1))), color='red', label='Regression Line')
ax.set_xlabel('Distance to City Center (Miles)')
ax.set_ylabel('Number of Checkouts')
ax.set_title('Distance to City Center vs Checkouts')
ax.legend()
plt.show()
