{% for var in ContinuousHistogram_list %} {% endfor %} {% for var in ContinuousHistogram_list %} {% endfor %} {% for var in target_distribution_list %} {% endfor %} {% for row in target_distribution_list %} {% endfor %} {% for key, row in ContinuousFeaturesHistChart_df.iterrows() %} {% endfor %} {% for key, row in ContinuousFeaturesHistChart_df.iterrows() %} {% endfor %}

ExploriPy - Target Specific EDA

{{ title }}

List Of Fields

{% for field in ListOfFields %} {% endfor %}
Field Type
{{ loop.index0 + 1 }} {{field["name"]}} {{field["type"]}}

Categorical Vs Continuous

Null Values
Null Percentage
{% for key, row in NullValue.iterrows() %} {% endfor %}
Feature Percentage
{{row["Feature"]}} {{row["NullPercentage"]}}%

Null Percentage

Continuous Variables
    {% for field in ContinuousFeatures %}
  • {{ field }}
    • {% endfor %}
Categorical Variables
    {% for field in CategoricalFeatures %}
  • {{ field }}
    • {% endfor %}
Other Variables
    {% for field in OtherFeatures %}
  • {{ field }}
    • {% endfor %}

Target Variable - {{ target }}

Total Number of Records - {{ number_of_records }}

Total Number of Nulls - {{ number_of_nulls }}

Percentage of Nulls - {{ percentage_of_nulls }}

Statistics on {{ target }}

Statistic Value
Count {{target_statistics_dict['Count']|round(2)}}
Mean {{target_statistics_dict['Mean']|round(2)}}
Median {{target_statistics_dict['Median']|round(2)}}
Std Dev {{target_statistics_dict['STD']|round(2)}}
Min {{target_statistics_dict['Min']|round(2)}}
25% {{target_statistics_dict['TwentyFive']|round(2)}}
50% {{target_statistics_dict['Fifty']|round(2)}}
75% {{target_statistics_dict['SeventyFive']|round(2)}}
Max {{target_statistics_dict['Max']|round(2)}}
Variance {{target_statistics_dict['Variance']|round(4)}}
Kurtosis {{target_statistics_dict['kurtosis']|round(4)}}
skew {{target_statistics_dict['skew']|round(4)}}

{{ target }} - Distribution

Categorical Vs Target

Influencing Categorical Variables - Based on ANOVA

{% for key, row in Anova_df.iterrows() %} {% endfor %}
Feature PValue
{{row["Categorical"]}} {{row["PValue"]|round(3)}}
Target Distribution for Categorical Variables
{% for var in target_distribution_list %}

{{ var['feature'] }}

{% for key, row in var['CategoriesCount_df'].iterrows() %} {% endfor %}
Category (Top 30) Number of Records
{{key}} {{row["Value"]}}

Categories with Similar Distributions

{% for key, row in var['GroupTukeyHSD_df'].iterrows() %}
{% for category in row['lists'] %} {% endfor %}
Dist- {{ row['list_name'] }}
{{ category }}
{% endfor %}

Null Percentage in Target (Top 10 Categories)

{% for field in var['target_null_distribution'] %} {% endfor %}
Category Null Percentage
{{ loop.index0 + 1 }} {{field["category"]}} {{field["null_percent"]}}%
{% endfor %}

Continuous Vs Target

Dependent Continuous Variables - Based on Correlation

{% for key, row in corr_features_df.iterrows() %} {% endfor %}
Feature Correlation
{{row['ContinuousFeature']}} {{row['Correlation']}}
Distribution of Continuous Variables for Target Categories
{% for key, row in ContinuousFeaturesHistChart_df.iterrows() %}

{{ row['ContinuousFeature'] }}

Statistic Value
Count {{row['Count']|round(2)}}
Mean {{row['Mean']|round(2)}}
Median {{row['Median']|round(2)}}
Std Dev {{row['STD']|round(2)}}
Min {{row['Min']|round(2)}}
25% {{row['TwentyFive']|round(2)}}
50% {{row['Fifty']|round(2)}}
75% {{row['SeventyFive']|round(2)}}
Max {{row['Max']|round(2)}}
Variance {{row['Variance']|round(4)}}
Kurtosis {{row['kurtosis']|round(4)}}
skew {{row['skew']|round(4)}}
{% endfor %}

Continuous Vs Continuous

Correlation Heatmap