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correltation_sj.py
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correltation_sj.py
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#%matplotlib inline
from __future__ import print_function
from __future__ import division
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
import statsmodels.api as sm
# just for the sake of this blog post!
from warnings import filterwarnings
filterwarnings('ignore')
# load the provided data
train_features = pd.read_csv('data/dengue_features_train.csv',
index_col=[0,1,2])
train_labels = pd.read_csv('data/dengue_labels_train.csv',
index_col=[0,1,2])
# Seperate data for San Juan
sj_train_features = train_features.loc['sj']
sj_train_labels = train_labels.loc['sj']
print('San Juan')
print('features: ', sj_train_features.shape)
print('labels : ', sj_train_labels.shape)
sj_train_features.head()
# Remove `week_start_date` string.
sj_train_features.drop('week_start_date', axis=1, inplace=True)
# Null check
pd.isnull(sj_train_features).any()
(sj_train_features
.ndvi_ne
.plot
.line(lw=0.8))
plt.title('Vegetation Index over Time')
plt.xlabel('Time')
sj_train_features.fillna(method='ffill', inplace=True)
print('San Juan')
print('mean: ', sj_train_labels.mean()[0])
print('var :', sj_train_labels.var()[0])
sj_train_labels.hist()
sj_train_features['total_cases'] = sj_train_labels.total_cases
# compute the correlations
sj_correlations = sj_train_features.corr()
# plot san juan
sj_corr_heat = sns.heatmap(sj_correlations)
plt.title('San Juan Variable Correlations')
# San Juan
(sj_correlations
.total_cases
.drop('total_cases') # don't compare with myself
.sort_values(ascending=False)
.plot
.barh())