Program 1 Seaborn Dataset # Heat Map Plot import seaborn as sns import matplotlib.pyplot as plt import numpy as np import seaborn as sns import pandas as pd # array_2d=np.linspace(1,7,15).reshape(5,3) # #print(array_2d) # sns.heatmap(array_2d)...
Program 1 import seaborn as sns import matplotlib.pyplot as plt import numpy as np import seaborn as sns df_tips=sns.load_dataset(‘tips’) print(df_tips.info()) plt.figure(figsize=(15,10)) sns.scatterplot(x=”total_bill”,y=”tip”,hue=’time’,style=’smoker’,data=df_tips) plt.title(“Scatter plot of Bill vs Tip”) plt.xlabel(“Total Bill”) plt.ylabel(“Tip”) plt.show() Program 2...
Program 1 Seaborn Dataset 1 Seaborn Dataset 2 #ecdf= Empirical Cumulative Distribution Function plot import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # df_value=pd.read_csv(“D://scikit_data/Billing/data.csv”) # print(df_value)...
Program 1 import seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np # Example DataFrame df = pd.DataFrame({“marks”: np.random.randint(40, 100, size=200)}) #print(df[‘marks’]) sns.histplot(df[‘marks’], bins=10, kde=True, color=’orange’) plt.title(“Student Marks...
Program 1 import seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np data = { ‘Fruit’: [‘Apple’, ‘Mango’, ‘Apple’, ‘Mango’, ‘Apple’, ‘Mango’], ‘Sales’: [100, 80, 90, 70, 95,...
Program 1 Seaborn Dataset import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns df_tips=pd.read_csv(“D://scikit_data/Billing/tips.csv”) #print(df_tips.head()) #print(df_tips.info()) # sns.lineplot(x=”size”,y=”total_bill”,data=df_tips,hue=’sex’, style=’sex’,palette=’Accent’,dashes=False,legend=’auto’,markers=[‘.’,’*’],size=1.0) # plt.title(“Information (Bill Vs Size)”,fontsize=15) # plt.xlabel(“No of...
Program 1 Seaborn Dataset import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # days=[1,2,3,4,5,6,7,8,9,10] # ind_temp=[34.4,41.4,45.4,24.4,34.4,36.2,44.4,33.2,28.7,33.5] # # Using matplot # plt.plot(days,ind_temp) # plt.xlabel(“Days”) # plt.ylabel(“Temprature”)...
Program 1 Seaborn Dataset import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns #df_tips=sns.load_dataset(“tips”) df_tips=pd.read_csv(“D://scikit_data/Billing/tips.csv”) # print(df_tips.head()) # print(df_tips.isnull().sum()) # print(df_tips.shape) # print(df_tips.info()) sns.lineplot(x=”total_bill”,y=”time”,data=df_tips) plt.title(“Billing System Graph(...
Program 1 import pandas as pd from sklearn.datasets import load_digits import matplotlib.pyplot as plt digits=load_digits() digits dir(digits) digits.target digits.data plt.gray() plt.matshow(digits.images[4]) plt.show() for i in range(5): plt.matshow(digits.images[i]) digits.target digits.target[:4] # Data Frame df=pd.DataFrame(digits.data) df.head()...
Program 1 import pandas as pd import numpy as np from sklearn import tree from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split df_titanic=pd.read_csv(“D://scikit_data/titanicData/titanic.csv”) df_titanic df_titanic.shape df_titanic.info() # Find Missing values df_titanic.isnull().sum() df_titanic=df_titanic.dropna() df_titanic.isnull().sum() df_titanic.shape...
