<jupyter_start><jupyter_text>ploting bar chat<jupyter_code>import matplotlib.pyplot as plt
import numpy as np
from matplotlib import style

#Data set of indian artificial intelligence production class

classes = ["python","AI","ML","R","DS"]

cla1_stud = [ 30,20,10,20,30]
cla2_stud = [ 15,30,15,20,10]
cla3_stud = [ 10,20,16,34,23]
plt.bar(classes,cla1_stud )
plt.barh(classes,cla1_stud)
plt.bar(classes,cla1_stud, 
        width = 0.2,
        color = "y", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 1 stud",visible = True ,
        animated =True,
        capstyle ='round'

       )
plt.figure(figsize = (15,15)) 
style.use("ggplot")

plt.figure(figsize = (15,9))

plt.bar(classes,cla1_stud, 
        width = 0.5,
        color = "y", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 1 stud",visible = True ,
        animated =True,
        capstyle ='round',
        linewidth = 5

       )

plt.title("Bar chat of AIP class" ,fontsize = 18 ,color = "b")
plt.xlabel("classes", fontsize = 15,color = "b") 
plt.ylabel("no of student" ,fontsize = 15, color = "b")
 
plt.figure(figsize = (15,9))



plt.bar(classes,cla1_stud, 
        width = 0.3,
        color = "y", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 1 stud",visible = True ,
       

       )

plt.bar(classes,cla2_stud, 
        width = 0.3,
        color = "m", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 2 stud",
        visible = True ,
       

       )

plt.title("Bar chat of AIP class" ,fontsize = 18 ,color = "b")
plt.xlabel("classes", fontsize = 15,color = "b") 
plt.ylabel("no of student" ,fontsize = 15, color = "b")
plt.figure(figsize = (15,9))

classes_index = np.arange(len(classes))

width = 0.2



plt.bar(classes_index,cla1_stud, 
        width,
        color = "y", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 1 stud"
       

       )

plt.bar(classes_index + width ,cla2_stud, 
         width,
        color = "m", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 2 stud"
     
       

       )
plt.bar(classes_index + width + width ,cla3_stud, 
        width,
        color = "b", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 3 stud"
     
        )


plt.xticks( classes_index + width ,classes,rotation = 20)

plt.title("Bar chat of AIP class" ,fontsize = 18 ,color = "b")
plt.xlabel("classes", fontsize = 15,color = "b") 
plt.ylabel("no of student" ,fontsize = 15, color = "b")
plt.legend()
plt.show()
plt.figure(figsize = (15,9))

classes_index = np.arange(len(classes))

width = 0.2



plt.barh(classes_index,cla1_stud, 
        width,
        color = "y", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 1 stud"
       

       )

plt.barh(classes_index + width ,cla2_stud, 
         width,
        color = "m", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 2 stud"
     
       

       )
plt.barh(classes_index + width + width ,cla3_stud, 
        width,
        color = "b", 
        edgecolor = "k",
        linestyle = "--",
        label = "class 3 stud"
     
        )


plt.yticks( classes_index + width ,classes,rotation = 20)

plt.title("Bar chat of AIP class" ,fontsize = 18 ,color = "b")
plt.ylabel("classes", fontsize = 15,color = "b") 
plt.xlabel("no of student" ,fontsize = 15, color = "b")
plt.legend()
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15358361774744028, 'fraction_numerical': 0.03185437997724687, 'mean_word_length': 2.109637488947834, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '594471', 'license': 'MIT', 'n_tokens_mistral': 1379, 'n_tokens_neox': 1237, 'n_words': 284, 'path': 'bar  chart.ipynb', 'repo_name': 'akshhpatil/Matplotlib-demo', 'size': '3516'}