adding all files done so far

bayes-learning/seaborn-graphing.py

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+import pandas as pd
+import numpy as np
+import os
+import matplotlib
+import matplotlib.pyplot as plt
+from scipy.stats import trim_mean
+from scipy.stats.mstats import mode, gmean, hmean
+from sklearn.model_selection import train_test_split
+import seaborn as sns
+
+
+def linebreak():
+    """prints a line break to split up functions"""
+    print('\n ============================================== \n')
+
+
+matplotlib.rcParams['backend'] = 'TkAgg'
+# plt.style.use('seaborn-dark-palette')
+
+path = os.getcwd()
+data_file = str('/data/Social_Network_Ads.csv')
+
+df = pd.read_csv(path + data_file)
+
+df = df.sample(frac=1).reset_index(drop=True)
+
+print(df[0:5])
+
+X = df[['Age', 'EstimatedSalary']]
+y = df['Purchased']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
+
+
+# ax1 = df.plot.scatter(x='Age', y='EstimatedSalary', c='DarkBlue')
+# ax2 = df.query('Age < 30').plot.scatter(x='Age', y='EstimatedSalary',
+# c='DarkBlue')
+
+
+# figure_1 = df.query('Age < 35').plot(kind='scatter', x='Age',
+# y='EstimatedSalary')
+
+df_purchased_sum = df['Purchased'].value_counts()
+
+# figure_2 = plt.plot(df_purchased_sum)
+
+# cp = sns.countplot(data=df, y='Purchased')
+
+# pal = dict(1="seagreen", 0="gray")
+
+fig, axs = plt.subplots(ncols=2)
+
+sns.countplot(data=df, x='Age', hue='Purchased', ax=axs[0])
+cp = sns.countplot(data=df, x='Purchased', ax=axs[1])
+
+plt.show()
+
+# print(df_purchased_sum)