In [1]:
#Import required packages
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import datetime
import seaborn as sns
import matplotlib.pyplot as plt
In [6]:
def format_date(df_date):
"""
Splits Meeting Times and Dates into datetime objects where applicable using regex.
"""
df_date['Days'] = df_date['Meeting_Times'].str.extract('([^\s]+)', expand=True)
df_date['Start_Date'] = df_date['Meeting_Dates'].str.extract('([^\s]+)', expand=True)
df_date['Year'] = df_date['Term'].astype(str).str.slice(0,4)
df_date['Quarter'] = df_date['Term'].astype(str).str.slice(5,6)
df_date['Building'] = df_date['ROOM'].str.extract('([^\s]+)', expand=True)
#df_date['Start_Month'] = pd.to_datetime(df_date['Year'] + df_date['Start_Date'], format='%Y%b')
df_date['End_Date'] = df_date['Meeting_Dates'].str.extract('(?<=-)(.*)(?= )', expand=True)
#df_date['End_Month'] = pd.to_datetime(df_date['End_Date'], format='%b')
df_date['Start_Time'] = df_date['Meeting_Times'].str.extract('(?<= )(.*)(?=-)', expand=True)
df_date['Start_Time'] = pd.to_datetime(df_date['Start_Time'], format='%H%M')
df_date['End_Time'] = df_date['Meeting_Times'].str.extract('((?<=-).*$)', expand=True)
df_date['End_Time'] = pd.to_datetime(df_date['End_Time'], format='%H%M')
df_date['Duration_Hr'] = ((df_date['End_Time'] - df_date['Start_Time']).dt.seconds)/3600
#df_date = df_date.set_index(pd.DatetimeIndex(df_date['Start_Month']))
return df_date
def format_xlist(df_xl):
"""
revises % capacity calculations by using Max Enrollment instead of room capacity.
"""
df_xl['Cap_Diff'] = np.where(df_xl['Xlst'] != '',
df_xl['Max_Enrl'].astype(int) - df_xl['Actual_Enrl'].astype(int),
df_xl['Room_Capacity'].astype(int) - df_xl['Actual_Enrl'].astype(int))
df_xl = df_xl.loc[df_xl['Room_Capacity'].astype(int) < 999]
return df_xl
def grouped_plot_graph(df_in):
fig, ax = plt.subplots()
grouped = df_in.groupby(['Year', 'Quarter'])
for key, group in grouped:
group.plot(ax=ax, kind='scatter', x='Start_Month', y='Cap_Diff', label=key)
plt.show()
def plot_graph(x_vals, y_vals):
"""
Plots the dataframe information.
"""
x = range(len(x_vals))
plt.figure(figsize=(20,10))
N = 50
colors = np.random.rand(N)
sns.stripplot(x_vals, y_vals)
plt.xticks(rotation=90)
plt.scatter(x, y_vals, alpha=0.5, )
plt.show()
def OLS_operations(y, X):
#mod = smf.OLS('Cap_Diff ~ C(Dept)', data=df_data)
mod = sm.OLS(np.asarray(y), X)
res = mod.fit()
print(res.summary())
fig = plt.figure(figsize=(20,10))
fig = sm.graphics.plot_partregress_grid(mod, fig=fig)
plt.show()
#pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
df = pd.read_csv('data/PSU_master_classroom_91-17.csv')
df = df.fillna('')
df = format_date(df)
# Avoid classes that only occur on a single day
df = df.loc[df['Start_Date'] != df['End_Date']]
df = df.loc[df['Online Instruct Method'] != 'Fully Online']
#df = df.loc[df['Term'] == 201604]
# Calculate number of days per week and treat Sunday condition
df['Days_Per_Week'] = df['Days'].str.len()
df['Room_Capacity'] = df['Room_Capacity'].apply(lambda x: x if (x != 'No Data Available') else 0)
df_cl = df_cl.loc[df_cl['Term'] < 201701]
df_cl = format_xlist(df)
In [7]:
dep_var = df_cl['Cap_Diff'] # Our dependent variable
category = 'Dept'
test_var = df_cl['{0}'.format(category)]
plot_graph(test_var, dep_var)
In [8]:
def main():
pd.set_option('display.max_columns', None)
df = pd.read_csv('data/PSU_master_classroom_91-17.csv')
df = df.fillna('')
df = format_date(df)
# Avoid classes that only occur on a single day
df = df.loc[df['Start_Date'] != df['End_Date']]
df = df.loc[df['Online Instruct Method'] != 'Fully Online']
df['Days_Per_Week'] = df['Days'].str.len()
df['Room_Capacity'] = df['Room_Capacity'].apply(lambda x: x if (x != 'No Data Available') else 0)
df_cl = format_xlist(df)
dep_var = df_cl['Cap_Diff'] # Our dependent variable
category = 'Building'
test_var = df_cl['{0}'.format(category)]
plot_graph(test_var, dep_var)
main()
In [9]:
def main():
pd.set_option('display.max_columns', None)
df = pd.read_csv('data/PSU_master_classroom_91-17.csv')
df = df.fillna('')
df = format_date(df)
# Avoid classes that only occur on a single day
df = df.loc[df['Start_Date'] != df['End_Date']]
df = df.loc[df['Online Instruct Method'] != 'Fully Online']
df['Days_Per_Week'] = df['Days'].str.len()
df['Room_Capacity'] = df['Room_Capacity'].apply(lambda x: x if (x != 'No Data Available') else 0)
df_cl = format_xlist(df)
dep_var = df_cl['Cap_Diff'] # Our dependent variable
category = 'Class'
test_var = df_cl['{0}'.format(category)]
plot_graph(test_var, dep_var)
main()