If anyone with design sensibilities sees this. We are open to changing the project logo.

We like the pandas logo for example https://github.com/pandas-dev/pandas

Hi AutoViML,

Firstly, congratulations and thanks for this wonderful package. This works perfectly fine with Jupyter notebooks but how do I use the same if I am using an IDE let say Spyder?

Thanks in advance. Mohit

hello Ram, when i run the code on my dateset, dft = av.AutoViz('', sep, target, df) i get this error "Not able to read or load file. Please check your inputs and try again..."

what could the issue be?

Similar project to AutoViz are Sweetviz and Pandas Profiling.

They could export the report as a HTML file. I wonder if this library also has this function?

Hi, I gave input csv contains 20 variables,while preprocessing it removed all important columns,may i know the reason?. note:- removed columns contains fill data without null values

I am having the above error while using autoviz My target variable is price i even tried it using empty target variable still same result i dont know the problem I tried dataframe, still same issue meanwhile i as able to read my data with pandas but autoviz could no house_price.zip i attached Zip file of the data because it was large kindly assist

1. Updating Stopwords List

Currently, I can see that Stopwords are defined as a list and I can see that it is missing a few stop words like "themselves".

def return_stop_words():
    STOP_WORDS = ['it', "this", "that", "to", 'its', 'am', 'is', 'are', 'was', 'were', 'a',
                'an', 'the', 'and', 'or', 'of', 'at', 'by', 'for', 'with', 'about', 'between',
                 'into','above', 'below', 'from', 'up', 'down', 'in', 'out', 'on', 'over',
                  'under', 'again', 'further', 'then', 'once', 'all', 'any', 'both', 'each',
                   'few', 'more', 'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so',
                    'than', 'too', 'very', 's', 't', 'can', 'just', 'd', 'll', 'm', 'o', 're',
                    've', 'y', 'ain', 'ma']
    add_words = ["s", "m",'you', 'not',  'get', 'no', 'via', 'one', 'still', 'us', 'u','hey','hi','oh','jeez',
                'the', 'a', 'in', 'to', 'of', 'i', 'and', 'is', 'for', 'on', 'it', 'got','aww','awww',
                'not', 'my', 'that', 'by', 'with', 'are', 'at', 'this', 'from', 'be', 'have', 'was',
                '', ' ', 'say', 's', 'u', 'ap', 'afp', '...', 'n', '\\']
    stop_words = list(set(STOP_WORDS+add_words))
    return sorted(stop_words)

Isn't it better to use NLTK stop words list??

from nltk.corpus import stopwords

for lang in langs:
  stopwords.words(lang)

Copied from: https://gist.github.com/sebleier/554280

2. Lemmatization before plotting

I think it is better if we lemmatize the data before we plot then words like "reads", "reading" will count as the same, which will give us a better word cloud.

Hi Ram, I have tried this package and found out a potential bug. When I tried to do the AV.AutoViz('', ',', 'target', df) to run an autoViz stuff, the x y labels of each graph are misplaced (x label should be placed at y label and vice versa.). I have tried two datasets and it still happened. Please look into this and see if this is a bug or I just did something wrong. Thanks! Jeff

requirements.txt have an dependecie "panel" in 12.x version. is too old. new version of panel is 0.14.x and have a new bugfixes.

Suggestion: panel~=0.12.6 ---> support new panel version (0.14.0)

Shape of your Data Set loaded: (3312, 20) ####################################################################################### ######################## 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... 20 Predictors classified... 2 variables removed since they were ID or low-information variables Number of All Scatter Plots = 6 Could not draw wordcloud plot for Order ID Could not draw wordcloud plot for Product ID Could not draw wordcloud plot for Product Name Could not draw wordcloud plot for Customer ID Could not draw wordcloud plot for Customer Name Could not draw wordcloud plot for City Time to run AutoViz = 5 seconds

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

Here is minimal reproducible example with google colab:

1. Date time column is no recognized, when input is file:

!pip install autoviz
from autoviz.AutoViz_Class import AutoViz_Class

import pandas as pd

AV = AutoViz_Class()

df = pd.DataFrame({'time': ['2020-01-15', '2020-02-15', '2020-03-15', '2020-04-15', '2020-05-15'], 'values': [1.0,2.5,3.2,4.2,5.6]})
df['time'] = pd.to_datetime(df['time'])
df.to_csv('ts.csv', index=False)

dft = AV.AutoViz("ts.csv", verbose=2)

hape of your Data Set loaded: (5, 2)
############## 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...
Data Set Shape: 5 rows, 2 cols
Data Set columns info:
* time: 0 nulls, 5 unique vals, most common: {'2020-05-15': 1, '2020-03-15': 1}
* values: 0 nulls, 5 unique vals, most common: {3.2: 1, 5.6: 1}
--------------------------------------------------------------------
    Numeric Columns: ['values']
    Integer-Categorical Columns: []
    String-Categorical Columns: []
    Factor-Categorical Columns: []
    String-Boolean Columns: []
    Numeric-Boolean Columns: []
    Discrete String Columns: []
    NLP text Columns: []
    Date Time Columns: []
    ID Columns: ['time']
    Columns that will not be considered in modeling: []
    2 Predictors classified...
        This does not include the Target column(s)
        1 variables removed since they were ID or low-information variables
    List of variables removed: ['time']
No categorical or numeric vars in data set. Hence no bar charts.
Time to run AutoViz (in seconds) = 0.562

2. When input is dataframe - chart is not generated, but date time column is recognized:

dft = AV.AutoViz("", dfte=df, verbose=2)
Shape of your Data Set loaded: (5, 2)
############## 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...
Data Set Shape: 5 rows, 2 cols
Data Set columns info:
* time: 0 nulls, 5 unique vals, most common: {Timestamp('2020-05-15 00:00:00'): 1, Timestamp('2020-04-15 00:00:00'): 1}
* values: 0 nulls, 5 unique vals, most common: {3.2: 1, 5.6: 1}
--------------------------------------------------------------------
    Numeric Columns: ['values']
    Integer-Categorical Columns: []
    String-Categorical Columns: []
    Factor-Categorical Columns: []
    String-Boolean Columns: []
    Numeric-Boolean Columns: []
    Discrete String Columns: []
    NLP text Columns: []
    Date Time Columns: ['time']
    ID Columns: []
    Columns that will not be considered in modeling: []
    2 Predictors classified...
        This does not include the Target column(s)
        No variables removed since no ID or low-information variables found in data set
Could not draw Date Vars
No categorical or numeric vars in data set. Hence no bar charts.
Time to run AutoViz (in seconds) = 0.408

Expected result: chart with date on x-axis, and value on y-axis.

I personally found the readme very hard to read. I had to scroll to three quarters down the page to learn how to use the library. I think it would be better to move the new features at the top of the file to the rear half of the readme for advanced users, so that beginners can begin using the package easier.

When using vervose=2 to output an svg or png file, there is an issue where the top title part is cut off. There seems to be a problem with the height value setting, please check.