"""
 # **<span style="color:#346888;">Dataprep The easiest way to prepare data in Python</span>**
 
 DataPrep is free, open-source software released under the MIT license. Anyone can reuse DataPrep code for any purpose.

DataPrep is built using Pandas/Dask DataFrame and can be seamlessly integrated with other Python libraries.

DataPrep is designed for computational notebooks, the most popular environment among data scientists.

Dataprepare is an initiative by SFU Data Science Research Group to speed up Data Science.

Acknowlegdements

SFU Data Science Research Group - Simon Fraser University

https://sfu-db.github.io/dataprep/user_guide/eda/plot.html

https://dataprep.ai/

https://towardsdatascience.com/dataprep-eda-accelerate-your-eda-eb845a4088bc
"""
"""
![](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a0/2011_Census_India_literacy_distribution_map_by_states_and_union_territories.svg/1200px-2011_Census_India_literacy_distribution_map_by_states_and_union_territories.svg.png)wikipedia
"""
# 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

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt

from matplotlib.offsetbox import AnchoredText
from mpl_toolkits.axes_grid1 import make_axes_locatable

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
!pip install dataprep
from dataprep.eda import plot, plot_correlation, create_report, plot_missing
df = pd.read_csv('/kaggle/input/india-literacy-data-district-wise/Literacy Data 2011.csv')
df.head()
plot(df)
"""
#Numerical column, it computes column statistics, and generates a histogram, kde plot, box plot and qq-normal plot:
"""
plot(df, "Literacy")
"""
#Categorical column, it computes column statistics, and plots a bar chart and pie chart.
"""
"""
#It has WordCloud, WordFrequency and Word Lenght. Isn't that amazing!
"""
plot(df, "State")
"""
#The relationship between two columns with plot(df, x, y)

When x and y are both numerical columns, it generates a scatter plot, hexbin plot and box plot.
"""
plot(df, "Unnamed: 0","Literacy")
"""
#When x and y are both categorical columns, it plots a nested bar chart, stacked bar chart and heat map.
"""
plot(df, "District","State")
"""
#With one or two specific columns, it provides a more detailed plot for the specific columns by passing column names as the parameter.
"""
plot(df, "Literacy", bins=20)
"""
#Correlation matrix between columns.
"""
plot_correlation(df)
"""
#Correlation of specified element with all other attributes.
"""
plot_correlation(df, "Literacy")
"""
#Analyze missing values
"""
plot_missing(df)
from dataprep.eda import create_report

df = pd.read_csv("/kaggle/input/india-literacy-data-district-wise/Literacy Data 2011.csv")
create_report(df)
#Code by Olga Belitskaya https://www.kaggle.com/olgabelitskaya/sequential-data/comments
from IPython.display import display,HTML
c1,c2,f1,f2,fs1,fs2=\
'#eb3434','#eb3446','Akronim','Smokum',30,15
def dhtml(string,fontcolor=c1,font=f1,fontsize=fs1):
    display(HTML("""<style>
    @import 'https://fonts.googleapis.com/css?family="""\
    +font+"""&effect=3d-float';</style>
    <h1 class='font-effect-3d-float' style='font-family:"""+\
    font+"""; color:"""+fontcolor+"""; font-size:"""+\
    str(fontsize)+"""px;'>%s</h1>"""%string))
    
    
dhtml('Greetings from Marília Prata, definitely Not a Bot. @mpwolke was Here.' )