No plot visible in local Jupiter

[GDGauravDutta]

from autoviz.AutoViz_Class import AutoViz_Class %matplotlib AV = AutoViz_Class() df = AV.AutoViz(filename='',dfte=train,depVar='Species',verbose=1)

Using matplotlib backend: Qt5Agg Shape of your Data Set loaded: (150, 5) ############## C L A S S I F Y I N G V A R I A B L E S #################### Classifying variables in data set... Number of Numeric Columns = 4 Number of Integer-Categorical Columns = 0 Number of String-Categorical Columns = 0 Number of Factor-Categorical Columns = 0 Number of String-Boolean Columns = 0 Number of Numeric-Boolean Columns = 0 Number of Discrete String Columns = 0 Number of NLP String Columns = 0 Number of Date Time Columns = 0 Number of ID Columns = 0 Number of Columns to Delete = 0 4 Predictors classified... No variables removed since no ID or low-information variables found in data set

################ Multi_Classification VISUALIZATION Started ##################### Data Set Shape: 150 rows, 5 cols Data Set columns info:

• SepalLengthCm: 0 nulls, 35 unique vals, most common: {5.0: 10, 5.1: 9}
• SepalWidthCm: 0 nulls, 23 unique vals, most common: {3.0: 26, 2.8: 14}
• PetalLengthCm: 0 nulls, 43 unique vals, most common: {1.5: 14, 1.4: 12}
• PetalWidthCm: 0 nulls, 22 unique vals, most common: {0.2: 28, 1.3: 13}

• Species: 0 nulls, 3 unique vals, most common: {'Iris-setosa': 50, 'Iris-versicolor': 50}

  Columns to delete: ' []' Boolean variables %s ' []' Categorical variables %s ' []' Continuous variables %s " ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']" Discrete string variables %s ' []' Date and time variables %s ' []' ID variables %s ' []' Target variable %s ' Species' Total Number of Scatter Plots = 10 No categorical or boolean vars in data set. Hence no pivot plots... No categorical or numeric vars in data set. Hence no bar charts. Time to run AutoViz = 2 seconds

  ###################### AUTO VISUALIZATION Completed ########################

but no plot.

In kaggle it' was working fine

https://www.kaggle.com/gauravduttakiit/multi-classification-problem-iris/notebook

[AutoViML]

Hi @GDGauravDutta 👍 You didn't get the sequence of commands above right. You must do %matplotlib inline after you create the AutoViz_Class.

import pandas as pd
import numpy as np
###########  import Autoviz_Class ##################
from autoviz.AutoViz_Class import AutoViz_Class
###########  set up the data ##################
datapath = '../Ram/Data_Sets/'
file_name = 'iris.csv'
depVar = 'typeclass'
sep=','
###########  Create the autoviz_class ###############
AV = AutoViz_Class()
###########  do %matplotlib inline now ############
%matplotlib inline
###########  Now run Autoviz ##################
_ = AV.AutoViz(datapath+file_name)

This sequence above works fine and displays the charts in my Jupyter Notebook (laptop). AutoViML

[AutoViML]

Hi @GDGauravDutta 🥇 If you need to download a notebook that works and you can test you data on it: here it is

https://github.com/AutoViML/AutoViz/blob/master/Examples/AutoViz_Demo.ipynb

AutoViML