df = pd.read_csv("F:\\kaggleDataSet\\chennai-water\\chennai_reservoir_levels.csv") df["Date"] = pd.to_datetime(df["Date"], format='%d-%m-%Y') df.head() import datetime def scatter_plot(cnt_srs, color): trace = go.Scatter( x=c…

# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np…

# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import matplotlib.p…

# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np…

# import pandas import pandas as pd # creating a DataFrame pd.DataFrame({'Yes': [50, 31], 'No': [101, 2]}) # another example of creating a dataframe pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 'Sue': ['Pretty good.', 'Bland']}) pd.DataFram…

import pandas as pd # Data analysis import numpy as np #Data analysis import seaborn as sns # Data visualization import matplotlib.pyplot as plt # Data Visualization import matplotlib.gridspec as gridspec # subplots and grid from wordcloud import Wor…

import numpy as np import pandas as pd import os import matplotlib.pyplot as pl import seaborn as sns import warnings warnings.filterwarnings('ignore') data = pd.read_csv('F:\\kaggleDataSet\\MedicalCostPersonal\\insurance.csv') data.head() data.isnul…

#We import libraries for linear algebra, graphs, and evaluation of results import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.metrics impo…

import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mpimg import seaborn as sns %matplotlib inline np.random.seed(2) from sklearn.model_selection import train_test_split from sklearn.metrics import confus…

import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # plotly import chart_studio.plotly as py from plotly.offline import init_notebook_mode, iplot init_notebook_mode(connected=True) import plotly…