pygaurav

# Simple Decorator Code Starts
# a = {"isAdmin":True}


# def custom_decorators(func):
#     def inner_function():
#         if a["isAdmin"]:
#             return func()
#     return inner_function

pygaurav

import pandas as pd
import numpy as np

dataframe1 = pd.DataFrame({"Class_ID":[1,2,3,4],
                           "Name":["Adam","Jack","Ram","Krishna"]})

dataframe2 = pd.DataFrame({"Class_ID":[2,3,4,10,11],
                           "Marks":[20,30,40,60,70]})

pygaurav

import pandas as pd
import numpy as np

dataframe1 = pd.DataFrame({"Class_ID":[1,2,3,4],
                           "Name":["Adam","Jack","Ram","Krishna"]})

dataframe2 = pd.DataFrame({"Class_ID":[2,3,4,10,11],
                           "Marks":[20,30,40,60,70]})

pygaurav

import pandas as pd
import numpy as np

dataframe1 = pd.DataFrame({"Class_ID":[1,2,3,4],
                           "Name":["Adam","Jack","Ram","Krishna"]})

dataframe2 = pd.DataFrame({"Class_ID":[2,3,4,10,11],
                           "Marks":[20,30,40,60,70]})

pygaurav

import pandas as pd
import numpy as np

dataframe1 = pd.DataFrame({"Class_ID":[1,2,3,4],
                           "Name":["Adam","Jack","Ram","Krishna"]})

dataframe2 = pd.DataFrame({"Class_ID":[2,3,4,10,11],
                           "Marks":[20,30,40,60,70]})

pygaurav

import pandas as pd
import numpy as np

dataframe1 = pd.DataFrame({"Class_ID":[1,2,3,4],
                           "Name":["Adam","Jack","Ram","Krishna"]})

dataframe2 = pd.DataFrame({"Class_ID":[2,3,4,10,11],
                           "Marks":[20,30,40,60,70]})

pygaurav

import pandas as pd
import numpy as np

#Get the mean of the group less than 5
def get_mean_group_lessthan_five(arr):
    return arr[(arr<5)].mean()

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)

pygaurav

import pandas as pd
import numpy as np

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)
#Check the mean sepal length, sepal width, petal length, petal width on the
#basis of type of flower
abc = df.groupby('Type of flower').mean()
print(abc)

pygaurav

import pandas as pd
import numpy as np

# Method for Ranking the Category
# if the mean is less than 4 it will return 1 category else 2
def category_name(arr):
    mean = arr.mean()
    if mean>4:
        return 1
    else:

pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)

abc = df.agg(["mean","sum","std"])
print(abc)