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How to use find_cls method in autotest

Best Python code snippet using autotest_python

Mall Customer Segmentation Data.py

Source:Mall Customer Segmentation Data.py

1#!/usr/bin/env python2# coding: utf-83# In[3]:4## For data manupilation5import numpy as np6import pandas as pd7# For os8import os9# remove warning10import warnings11warnings.filterwarnings("ignore")12# In[4]:13os.chdir("D:\\data analyst data\\download data\\Mall Customer Segmentation Data")14os.listdir()15# In[5]:16df = pd.read_csv('Mall_Customers.csv')17df.head() 18# In[6]:19df.isnull().sum() # checking if there is any null value 20# In[7]:21df.rename(columns= {'Annual Income (k$)' : 'Annual_Income', 'Spending Score (1-100)' : 'Spending_Score'}, inplace = True)22# In[8]:23df.drop('CustomerID', axis = 1 ,inplace = True)24# In[9]:25df.dtypes # checking data type 26# In[10]:27df.shape #checking shape of data 28# In[11]:29df.columns.values #checking columns 30# In[12]:31# For creating colormaps32import matplotlib.pyplot as plt33import seaborn as sns34# for clustering 35from sklearn.cluster import KMeans36import time  # Measuring process time37from sklearn.preprocessing import StandardScaler #  For data processing38from sklearn.model_selection import train_test_split39from sklearn.tree import DecisionTreeClassifier as dt40# In[13]:41np.min(df.Age), np.max(df.Age)42# In[14]:43sns.distplot(df.Age) # diagrams 44# In[15]:45sns.distplot(df.Annual_Income, hue = 'Gender')46# In[ ]:47sns.distplot(df.Spending_Score)48# In[16]:49sns.jointplot(df.Age, df.Spending_Score)50# In[17]:51sns.jointplot(df.Annual_Income, df.Age , kind="kde")52# In[18]:53sns.jointplot(df.Annual_Income, df.Age , kind="hex")54# In[19]:55sns.jointplot(df.Annual_Income, df.Age , kind="reg")56# In[20]:57cust_group = df.groupby(['Gender','Age']).sum().reset_index()58cust_group59# In[21]:60df['Annual_Income'].min()61# In[22]:62df['Annual_Income'].max()63# In[23]:64df['Annual_Income_cat'] = pd.cut(65                       df['Annual_Income'],66                       bins = 3,67                       labels= ['low', 'medium','high']68                      )69df70# In[24]:71plt.figure(1 , figsize = (10 , 10))72sns.barplot(x = 'Annual_Income_cat', y = 'Age',hue = 'Gender', data = df, )73# In[25]:74plt.figure(figsize=(15,3))75sns.barplot(data=cust_group,x='Age',y='Spending_Score',hue='Gender')76# In[26]:77sns.pairplot(data=cust_group, hue='Gender')78# In[27]:79df.drop(columns=['Annual_Income_cat'],inplace= True)  #Dropping columns not needed80# In[28]:81df82# In[29]:83from sklearn.preprocessing import LabelEncoder as le84enc=le()85# In[31]:86df['Gender']=enc.fit_transform(df['Gender'])87df.head()88# In[32]:89find_cls=[]90for i in range(1,15):91    kmean = KMeans(n_clusters=i)92    kmean.fit(df)93    find_cls.append(kmean.inertia_)94# In[33]:95find_cls96# In[34]:97fig, axs = plt.subplots(figsize=(12,5))98sns.lineplot(range(1,15),find_cls, ax=axs,marker='X')99axs.axvline(5, ls="--", c="crimson") # CRIMSON is color, ls - line style100axs.axvline(6, ls="--", c="crimson")101plt.grid() #  square lines in back ground102plt.show103# In[35]:104kmean=KMeans(n_clusters=5) # we found that best clusters are 5105kmean.fit(df)106# In[36]:107kmean.inertia_108# In[37]:109kmean.cluster_centers_110# In[38]:111clust_centers=kmean.cluster_centers_112# In[39]:113df.head()114# In[40]:115kmean.labels_116# In[41]:117df['center_cluster']=kmean.labels_118# In[42]:119df120# In[47]:121fig,(ax1,ax2)=plt.subplots(nrows=1, ncols=2,figsize=(15,5))122sns.scatterplot(data = df, x = 'Age', y = 'Spending_Score', ax=ax1, hue = 'center_cluster',palette='Set1')  # good color use palette =1123sns.scatterplot(data = df, x='Annual_Income', y ='Spending_Score', ax=ax2, hue='center_cluster',palette='Set1')124# In[344]:125fig,(ax1,ax2)=plt.subplots(nrows=1, ncols=2,figsize=(15,5))126ax1.scatter(kmean.cluster_centers_[:,1], kmean.cluster_centers_[:,3],marker='X',color='red')      127ax2.scatter(kmean.cluster_centers_[:,2], kmean.cluster_centers_[:,3],marker='X',color='red')128# In[ ]:129# In[178]:130# Copy 'Age' column to another variable and then drop it131#     We will not use it in clustering132y = df['Age'].values133df.drop(columns = ['Age'], inplace = True)134# In[179]:135y...

student_module.py

Source:student_module.py

...35    def cal_result(self):36        self.total = self.java + self.php + self.python + self.nodejs37        self.percentage = self.total / 438    # find class39    def find_cls(self):40        if self.java < 35 or self.php < 35 or self.python < 35 or self.nodejs < 35:41            self.cls = "Fail"42        elif self.percentage >= 70:43            self.cls = "Distinction"44        elif self.percentage >= 60:45            self.cls = "1st Class"46        elif self.percentage >= 50:47            self.cls = "2nd Class"48        elif self.percentage >= 40:49            self.cls = "3rd Class"50        else:51            self.cls = "Pass Class"52# main method53if __name__ == "__main__":54    obj = Student()55    obj.get_stud(1, "hitesh", "malegaon", 54, 54, 35, 65)56    obj.cal_result()57    obj.find_cls()58    obj.display_stud()59    # objs = [Student() for i in range(3)]60    # for obj in objs:61    #     obj.get_Student()62    # for obj in objs:63    #     obj.cal_result()64    #     obj.find_cls()...

1_6_classes_childes_1.py

Source:1_6_classes_childes_1.py

...4# <Ð¸Ð¼Ñ ÐºÐ»Ð°ÑÑÐ° 1> : <Ð¸Ð¼Ñ ÐºÐ»Ð°ÑÑÐ° 2> <Ð¸Ð¼Ñ ÐºÐ»Ð°ÑÑÐ° 3> ... <Ð¸Ð¼Ñ ÐºÐ»Ð°ÑÑÐ° k>5# ÐÑÐ¾ Ð¾Ð·Ð½Ð°ÑÐ°ÐµÑ, ÑÑÐ¾ ÐºÐ»Ð°ÑÑ 1 Ð¾ÑÐ½Ð°ÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½ Ð¾Ñ ÐºÐ»Ð°ÑÑÐ° 2, ÐºÐ»Ð°ÑÑÐ° 3, Ð¸ Ñ. Ð´.6# ÑÐ»Ð¾Ð²Ð°ÑÑ Ð½Ð°ÑÐ»ÐµÐ´Ð½Ð¸Ðº : Ð¿ÑÐµÐ´ÐºÐ¸7child_parents_dict = {}8def find_cls(a, b):9    if a == b:10        return True11    elif a in child_parents_dict[b]:12        return True13    else:14        for c in child_parents_dict[b]:15            if find_cls(a, c):16                return True17        return False18for i in range(int(input())):19    inp = str(input()).split()20    child_parents_dict.update({inp[0]: set(inp[2:])})21for i in range(int(input())):22    inp = (str(input()).split())23    cls1, cls2 = inp[0], inp[1]24    if find_cls(cls1, cls2):25        print("Yes")26    else:...

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