from sklearn import datasets
import statsmodels.api as sm
import statsmodels.formula.api as smf
import pandas as pd
import numpy as np
import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set_theme()
import matplotlib.pyplot as plt
data_gms = pd.read_csv(‘cor_gms.csv’)
data_open = pd.read_csv(‘cor_open.csv’)
data_close = pd.read_csv(‘cor_close.csv’)
da_gms = data_gms.drop([‘date’], axis=1)
da_open = data_open.drop([‘date’], axis=1)
da_close = data_close.drop([‘date’], axis=1)
cor_gms = da_gms.corr()
cor_gms = da_gms.corr()
cor_close = da_close.corr()
plt.figure(figsize=(16, 12))
ax_gms = sns.heatmap(cor_gms,annot = True,center=0,linewidths = 5)
plt.figure(figsize=(16, 12))
ax_gms = sns.heatmap(cor_gms,annot = True,center=0,linewidths = 5)
plt.figure(figsize=(16, 12))
ax_gms = sns.heatmap(cor_gms,annot = True,center=0,linewidths = 5)
.
