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Interactive Visualization using Plotly

This kernel covers interactive visualization using Plotly . The following plots are covered

1.Pie Chart
2.Histogram
3.Scatter Plot
4.Bar Chart
5.Distribution Plots
6.Bubble Plot

# importing libraries
import numpy as np 
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
import category_encoders as ce
%matplotlib inline
# loading dataset 
df = pd.read_csv("../input/widsdatathon2020/training_v2.csv")
# Number of missing values in the data
df = df[df.columns[0:50]]
df.columns
Index(['encounter_id', 'patient_id', 'hospital_id', 'hospital_death', 'age',
       'bmi', 'elective_surgery', 'ethnicity', 'gender', 'height',
       'hospital_admit_source', 'icu_admit_source', 'icu_id', 'icu_stay_type',
       'icu_type', 'pre_icu_los_days', 'readmission_status', 'weight',
       'albumin_apache', 'apache_2_diagnosis', 'apache_3j_diagnosis',
       'apache_post_operative', 'arf_apache', 'bilirubin_apache', 'bun_apache',
       'creatinine_apache', 'fio2_apache', 'gcs_eyes_apache',
       'gcs_motor_apache', 'gcs_unable_apache', 'gcs_verbal_apache',
       'glucose_apache', 'heart_rate_apache', 'hematocrit_apache',
       'intubated_apache', 'map_apache', 'paco2_apache', 'paco2_for_ph_apache',
       'pao2_apache', 'ph_apache', 'resprate_apache', 'sodium_apache',
       'temp_apache', 'urineoutput_apache', 'ventilated_apache', 'wbc_apache',
       'd1_diasbp_invasive_max', 'd1_diasbp_invasive_min', 'd1_diasbp_max',
       'd1_diasbp_min'],
      dtype='object')
from plotly.offline import init_notebook_mode,iplot
import plotly.graph_objects as go
import cufflinks as cf
init_notebook_mode(connected=True)