In [2]:

import numpy as np
import pandas as pd
from factor_analyzer import FactorAnalyzer
import matplotlib.pyplot as plt
import seaborn as sns

dfWell = pd.read_csv('https://www.dropbox.com/s/170bc3dimgn8ru8/wellness.csv?dl=1')
dfWell.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5011 entries, 0 to 5010
Data columns (total 19 columns):
Date                   5011 non-null object
PlayerID               5011 non-null int64
Fatigue                5011 non-null int64
Soreness               5011 non-null int64
Desire                 5011 non-null int64
Irritability           5011 non-null int64
BedTime                5011 non-null object
WakeTime               5011 non-null object
SleepHours             5011 non-null float64
SleepQuality           5011 non-null int64
MonitoringScore        5011 non-null int64
Pain                   5011 non-null object
Illness                5011 non-null object
Menstruation           4995 non-null object
Nutrition              4174 non-null object
NutritionAdjustment    4266 non-null object
USGMeasurement         4843 non-null object
USG                    629 non-null float64
TrainingReadiness      5011 non-null object
dtypes: float64(2), int64(7), object(10)
memory usage: 743.9+ KB

In [3]:

dfWell.head()

Out[3]:

	Date	PlayerID	Fatigue	Soreness	Desire	Irritability	BedTime	WakeTime	SleepHours	SleepQuality	MonitoringScore	Pain	Illness	Menstruation	Nutrition	NutritionAdjustment	USGMeasurement	USG	TrainingReadiness
0	2018-07-21	1	3	3	2	3	23:00:00	07:00:00	8.00	2	13	No	No	Yes	Excellent	Yes	No	NaN	0%
1	2018-07-21	2	4	3	4	4	23:00:00	07:00:00	8.00	4	19	Yes	No	Yes	NaN	NaN	Yes	1.010	0%
2	2018-07-21	3	3	3	5	4	22:30:00	06:30:00	8.00	4	19	No	No	No	NaN	NaN	Yes	1.016	100%
3	2018-07-21	4	2	3	5	4	00:30:00	07:00:00	6.50	1	15	No	No	Yes	Excellent	Yes	Yes	1.025	95%
4	2018-07-21	5	5	3	4	4	23:45:00	07:00:00	7.25	4	20	No	No	No	Okay	Yes	Yes	1.022	100%

In [4]:

dfWell.Menstruation.value_counts()

Out[4]:

No     4285
Yes     710
Name: Menstruation, dtype: int64

In [5]:

dfWell.Nutrition.value_counts()

Out[5]:

Excellent    2713
Okay         1398
Poor           63
Name: Nutrition, dtype: int64

In [6]:

dfWell.NutritionAdjustment.value_counts()

Out[6]:

Yes             3727
No               439
I Don't Know     100
Name: NutritionAdjustment, dtype: int64

In [7]:

# As Menstruation, Nutrition and Nutrition Adjustment are categorical, fill NA with their Modes
dfWell.Menstruation.fillna(dfWell.Menstruation.mode()[0],inplace=True)
dfWell.Nutrition.fillna(dfWell.Nutrition.mode()[0],inplace=True)
dfWell.NutritionAdjustment.fillna(dfWell.NutritionAdjustment.mode()[0],inplace=True)
dfWell.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5011 entries, 0 to 5010
Data columns (total 19 columns):
Date                   5011 non-null object
PlayerID               5011 non-null int64
Fatigue                5011 non-null int64
Soreness               5011 non-null int64
Desire                 5011 non-null int64
Irritability           5011 non-null int64
BedTime                5011 non-null object
WakeTime               5011 non-null object
SleepHours             5011 non-null float64
SleepQuality           5011 non-null int64
MonitoringScore        5011 non-null int64
Pain                   5011 non-null object
Illness                5011 non-null object
Menstruation           5011 non-null object
Nutrition              5011 non-null object
NutritionAdjustment    5011 non-null object
USGMeasurement         4843 non-null object
USG                    629 non-null float64
TrainingReadiness      5011 non-null object
dtypes: float64(2), int64(7), object(10)
memory usage: 743.9+ KB

In [8]:

dfWell.Menstruation.value_counts()

Out[8]:

No     4301
Yes     710
Name: Menstruation, dtype: int64

In [9]:

dfWell.Nutrition.value_counts()

Out[9]:

Excellent    3550
Okay         1398
Poor           63
Name: Nutrition, dtype: int64

In [10]:

dfWell.NutritionAdjustment.value_counts()

Out[10]:

Yes             4472
No               439
I Don't Know     100
Name: NutritionAdjustment, dtype: int64

In [11]:

# As USG measurement has almost 87% NA, it doesn't make sense to fill NA with random or mean values. 
# Hence, drop USG related columns
dfWell = dfWell.drop(['USGMeasurement', 'USG'], axis=1)
dfWell.head()

Out[11]:

	Date	PlayerID	Fatigue	Soreness	Desire	Irritability	BedTime	WakeTime	SleepHours	SleepQuality	MonitoringScore	Pain	Illness	Menstruation	Nutrition	NutritionAdjustment	TrainingReadiness
0	2018-07-21	1	3	3	2	3	23:00:00	07:00:00	8.00	2	13	No	No	Yes	Excellent	Yes	0%
1	2018-07-21	2	4	3	4	4	23:00:00	07:00:00	8.00	4	19	Yes	No	Yes	Excellent	Yes	0%
2	2018-07-21	3	3	3	5	4	22:30:00	06:30:00	8.00	4	19	No	No	No	Excellent	Yes	100%
3	2018-07-21	4	2	3	5	4	00:30:00	07:00:00	6.50	1	15	No	No	Yes	Excellent	Yes	95%
4	2018-07-21	5	5	3	4	4	23:45:00	07:00:00	7.25	4	20	No	No	No	Okay	Yes	100%

In [12]:

# We do not require Bed Time and Wake Time as well. We'll be using SleepHours instead
dfWell = dfWell.drop(['BedTime', 'WakeTime'], axis=1)
# Convert TrainingReadiness from String to fraction
dfWell['TrainingReadiness'] = dfWell['TrainingReadiness'].str.rstrip('%').astype('float') / 100.0 * 7

In [13]:

dfWell.Pain.value_counts()

Out[13]:

No     4423
Yes     588
Name: Pain, dtype: int64

In [14]:

dfWell.Illness.value_counts()

Out[14]:

No              4456
Slightly Off     421
Yes              134
Name: Illness, dtype: int64

In [15]:

# Convert into numerical values
dfWell.Pain = np.where(dfWell.Pain == 'No', 1, 0)
dfWell.Menstruation = np.where(dfWell.Menstruation == 'No', 1, 0)
dfWell.Nutrition = np.where(dfWell.Nutrition == 'Poor', 0, np.where(dfWell.Nutrition == 'Okay', 1, 2))
dfWell.Illness = np.where(dfWell.Illness == 'Yes', 0, np.where(dfWell.Illness == 'Slightly Off', 1, 2))
dfWell.NutritionAdjustment = np.where(dfWell.NutritionAdjustment == 'No', 0, \
                                      np.where(dfWell.NutritionAdjustment == 'I Don\'t Know', 1, 2))
dfWell.head()

Out[15]:

	Date	PlayerID	Fatigue	Soreness	Desire	Irritability	SleepHours	SleepQuality	MonitoringScore	Pain	Illness	Menstruation	Nutrition	NutritionAdjustment	TrainingReadiness
0	2018-07-21	1	3	3	2	3	8.00	2	13	1	2	0	2	2	0.00
1	2018-07-21	2	4	3	4	4	8.00	4	19	0	2	0	2	2	0.00
2	2018-07-21	3	3	3	5	4	8.00	4	19	1	2	1	2	2	7.00
3	2018-07-21	4	2	3	5	4	6.50	1	15	1	2	0	2	2	6.65
4	2018-07-21	5	5	3	4	4	7.25	4	20	1	2	1	1	2	7.00

In [16]:

from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity
df = dfWell[dfWell.columns.difference(['Date','PlayerID','MonitoringScore'])]
chi_square_value, p_value = calculate_bartlett_sphericity(df)
chi_square_value, p_value

Out[16]:

(10209.44506669854, 0.0)

In [17]:

from factor_analyzer.factor_analyzer import calculate_kmo
kmo_all, kmo_model = calculate_kmo(df)
kmo_model

Out[17]:

0.7230727699430893

In [18]:

# Create factor analysis object and perform factor analysis
fa = FactorAnalyzer()
fa.analyze(df, 12, rotation=None)
# Check Eigenvalues
ev, v = fa.get_eigenvalues()
ev

Out[18]:

	Original_Eigenvalues
0	2.867840
1	1.550185
2	1.163748
3	1.079430
4	0.990381
5	0.955426
6	0.885087
7	0.663733
8	0.566753
9	0.500336
10	0.445287
11	0.331794

In [19]:

# Create scree plot using matplotlib
plt.scatter(range(1,df.shape[1]+1),ev)
plt.plot(range(1,df.shape[1]+1),ev)
plt.title('Scree Plot')
plt.xlabel('Factors')
plt.ylabel('Eigenvalue')
plt.grid()
plt.show()

In [20]:

fa.analyze(df, 4, rotation="varimax")
fa.loadings

Out[20]:

	Factor1	Factor2	Factor3	Factor4
Desire	0.297775	0.014830	-0.018263	0.826713
Fatigue	0.680681	-0.005057	0.027876	0.481921
Illness	0.083225	0.071866	0.125495	0.110502
Irritability	0.469493	0.046278	0.004567	0.365455
Menstruation	0.022113	0.063481	0.020381	0.033328
Nutrition	0.047010	0.675161	0.051184	-0.029770
NutritionAdjustment	-0.015880	0.726706	-0.021174	-0.000230
Pain	0.030785	0.050629	0.995425	0.039629
SleepHours	0.334700	0.057831	0.020863	-0.088249
SleepQuality	0.713932	0.088429	0.078114	0.116014
Soreness	0.382954	-0.096350	0.098340	0.393524
TrainingReadiness	-0.057163	0.057130	0.055586	0.305239

In [21]:

fa.get_factor_variance()

Out[21]:

	Factor1	Factor2	Factor3	Factor4
SS Loadings	1.554890	1.021801	1.030533	1.334318
Proportion Var	0.129574	0.085150	0.085878	0.111193
Cumulative Var	0.129574	0.214724	0.300602	0.411795

In [22]:

df_fact = pd.DataFrame(np.dot(df, fa.loadings))
df_fact.columns = ["Energy","Nourishment","Discomfort","Determination"] 
df_fact

Out[22]:

	Energy	Nourishment	Discomfort	Determination
0	9.559896	3.501880	1.985388	5.102797
1	12.702699	3.698991	1.142109	7.796001
2	11.972553	4.232901	2.500879	10.350426
3	8.645920	3.802447	2.167529	9.512673
4	12.738104	3.489422	2.508064	10.583512
...	...	...	...	...
5006	18.614064	1.087246	2.825409	15.476140
5007	10.961311	3.214301	2.515009	9.804929
5008	12.822358	2.503752	2.477125	11.418906
5009	12.524053	3.412587	2.583743	10.473052
5010	14.886535	3.679469	2.736708	11.853125

5011 rows × 4 columns

In [23]:

dfWell = pd.concat([dfWell, df_fact], axis=1)
dfWell.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5011 entries, 0 to 5010
Data columns (total 19 columns):
Date                   5011 non-null object
PlayerID               5011 non-null int64
Fatigue                5011 non-null int64
Soreness               5011 non-null int64
Desire                 5011 non-null int64
Irritability           5011 non-null int64
SleepHours             5011 non-null float64
SleepQuality           5011 non-null int64
MonitoringScore        5011 non-null int64
Pain                   5011 non-null int32
Illness                5011 non-null int32
Menstruation           5011 non-null int32
Nutrition              5011 non-null int32
NutritionAdjustment    5011 non-null int32
TrainingReadiness      5011 non-null float64
Energy                 5011 non-null float64
Nourishment            5011 non-null float64
Discomfort             5011 non-null float64
Determination          5011 non-null float64
dtypes: float64(6), int32(5), int64(7), object(1)
memory usage: 646.1+ KB

In [24]:

dfWell = dfWell[["Date","PlayerID","MonitoringScore","Energy","Nourishment","Discomfort","Determination"]]
dfWell

Out[24]:

	Date	PlayerID	MonitoringScore	Energy	Nourishment	Discomfort	Determination
0	2018-07-21	1	13	9.559896	3.501880	1.985388	5.102797
1	2018-07-21	2	19	12.702699	3.698991	1.142109	7.796001
2	2018-07-21	3	19	11.972553	4.232901	2.500879	10.350426
3	2018-07-21	4	15	8.645920	3.802447	2.167529	9.512673
4	2018-07-21	5	20	12.738104	3.489422	2.508064	10.583512
...	...	...	...	...	...	...	...
5006	2017-08-01	8	33	18.614064	1.087246	2.825409	15.476140
5007	2017-08-01	10	18	10.961311	3.214301	2.515009	9.804929
5008	2017-08-01	12	21	12.822358	2.503752	2.477125	11.418906
5009	2017-08-01	13	20	12.524053	3.412587	2.583743	10.473052
5010	2017-08-01	14	24	14.886535	3.679469	2.736708	11.853125

5011 rows × 7 columns

In [25]:

df = dfWell[dfWell.columns.difference(['Date','PlayerID'])]
ax = sns.heatmap(df.corr(), annot=True, fmt=".2f")
bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)
plt.show()

In [26]:

dfWell = dfWell.drop(['MonitoringScore'], axis=1)
dfWell.head()

Out[26]:

	Date	PlayerID	Energy	Nourishment	Discomfort	Determination
0	2018-07-21	1	9.559896	3.501880	1.985388	5.102797
1	2018-07-21	2	12.702699	3.698991	1.142109	7.796001
2	2018-07-21	3	11.972553	4.232901	2.500879	10.350426
3	2018-07-21	4	8.645920	3.802447	2.167529	9.512673
4	2018-07-21	5	12.738104	3.489422	2.508064	10.583512