CS109A Introduction to Data Science¶

Lecture 2: Example¶

Harvard University
Fall 2019

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import pandas as pd
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


First Look At The Data¶

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hubway_data = pd.read_csv('hubway_trips.csv', low_memory=False)


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hubway_data.shape


A little data manipulation¶

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check_out_year = hubway_data['start_date'].apply(lambda s: int(s[-13:-9]))
year_to_age = (check_out_year - hubway_data['birth_date'])


Who¶

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fig, ax = plt.subplots(1, 2, figsize=(15, 6))

#And now for our first plot: a barplot of gender
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')

#And a histogram of ages

ax[1].hist(year_to_age.dropna(),bins=30)
ax[1].set_title('Histogram of User Ages by Checkout')
ax[1].axvline(x=np.mean(year_to_age.dropna()), color='red', label='Average Age')
ax[1].legend()

plt.show()

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#And now for a second plot: scatter plot of age with duration
plt.yscale('log')
plt.scatter(year_to_age,hubway_data.duration+1)
plt.title('Scatter plot of Duration by User Ages')
plt.xlabel('Age in years')
plt.ylabel('Duration (in seconds)')
plt.show()


Where¶

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station_data = pd.read_csv('hubway_stations.csv', low_memory=False)[['id', 'lat', 'lng']]

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hubway_data_with_gps = hubway_data.join(station_data.set_index('id'), on='strt_statn')


When¶

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#check_out_times = pd.to_datetime(hubway_data['start_date'])
check_out_hours = hubway_data['start_date'].apply(lambda s: int(s[-8:-6]))

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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¶

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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

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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.loc[:, 'dist_to_center'] = list(map(haversine, counts_df[['lng', 'lat']].values))

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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.savefig('How.png', dpi=300)

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#let's look at some subgroups
print(np.unique(hubway_data.subsc_type,return_counts=True))

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#And now for a plot of histograms across registration type
duration_registered = (hubway_data.duration[hubway_data.subsc_type == 'Registered'])
duration_casual = (hubway_data.duration[hubway_data.subsc_type == 'Casual'])

print(np.mean(duration_registered))
print(np.mean(duration_casual))

logduration_registered = np.log(duration_registered+1)
logduration_casual = np.log(duration_casual+1)

plt.hist(logduration_registered.dropna(), alpha=.5, bins=30)
plt.hist(logduration_casual.dropna(), alpha=.5, bins=30)

#plt.hist(duration_registered.dropna()+1, alpha=.5)
#plt.hist(duration_casual.dropna()+1, alpha=.5)
plt.title('Histograms of duration split by Checkouts')
plt.xlabel('Duration (in log(seconds))')
plt.ylabel('Number of Checkouts')
#plt.xscale('log')

plt.show()

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