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multi_participants/Graphing functions (3).py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from matplotlib import pyplot as plt 
+from matplotlib import rc
+import pylab as plb
+import pandas as pd
+import numpy as np
+
+
+# In[2]:
+
+
+data = pd.read_csv('/Users/kf/Downloads/case_4_accuracy.csv')
+
+
+# In[3]:
+
+
+standErr = np.std(data, ddof=1, axis=1) / np.sqrt(np.size(data, axis =1))
+
+
+# In[4]:
+
+
+data['mean'] = data.mean(axis=1)
+
+
+# In[16]:
+
+
+plt.figure(figsize=(7,8)) 
+plt.errorbar( data['Unnamed: 0'], data['mean'], yerr=standErr, fmt='o', color='Black', elinewidth=1,capthick=1,errorevery=1, alpha=1, ms=1, capsize = 3)
+plt.bar(data['Unnamed: 0'], data['mean'],tick_label = data['Unnamed: 0'], color = ['mediumpurple', 'cyan', 'b','r'],edgecolor='black')##Bar plot
+plt.yticks(rotation = 90)
+plt.xticks(size = 12)
+plt.yticks(size = 12)
+plt.xlabel("Classifier Type", size = 15)
+plt.ylabel("Precision", size = 15)
+ax = plt.gca()
+yticks = ax.yaxis.get_major_ticks() 
+yticks[0].label1.set_visible(False)
+plt.show()
+
+
+# In[ ]:
+
+
+
+