RPChinhara

import xml.etree.ElementTree as ET
import json

def parse_element(element, namespace, indent=""):
    result = {}

    if element.attrib:
        result['attributes'] = element.attrib

    if element.text and element.text.strip():

maria-aguilera

import numpy
import pandas
import matplotlib.pyplot as plt
import seaborn as sns
from ggplot import *
plt.style.use('ggplot')


def get_histogram_xy(data, bins=10):
    """Returns x,y coordinates for Histogram data.

maria-aguilera

"""
python3

numpy>=1.12.0
pandas>=0.19.2
matplotlib>=2.0.0
sklearn>=0.18
xgboost>=0.6
sortedcontainers>=1.5.7
"""

maria-aguilera

def plot_categorical_variables_pie(data, column_name, plot_defaulter = True, hole = 0):
    '''
    Function to plot categorical variables Pie Plots
    
    Inputs:
        data: DataFrame
            The DataFrame from which to plot
        column_name: str
            Column's name whose distribution is to be plotted
        plot_defaulter: bool

maria-aguilera

import seaborn as sns
from sklearn import preprocessing, ensemble
from scipy.stats import kendalltau
import pandas as pd
import random

#todo change module name
from tqdm import tqdm
import numpy as np
import pandas as pd

maria-aguilera

def plot_categorical(df: pd.DataFrame , col:str):
    """
    Function to plot the categorical data on piechart using Plotly
    @Args:
      df: pandas data frame
      col: A string column name within pandas data frame to plot
      
    Return:
      No object return, only visualization
    """

maria-aguilera

# Function to print digits on top of Barplot
def show_values(axs, orient="v", space=.01):
    def _single(ax):
        if orient == "v":
            for p in ax.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height() + (p.get_height()*0.01)
                value = '{:.2f}'.format(p.get_height())
                ax.text(_x, _y, value, ha="center") 
        elif orient == "h":

PranjalDureja0002

res_dim = 1024

if __name__ == "__main__":
    """loading the data, 
    reading the file annotations,
    appending the tabular coordinates to formulate a dataframe
    """
    df_org = pd.DataFrame()  
    directory = '/content/drive/MyDrive/data_cs2'
    final_col_directory = '/content/drive/MyDrive/cs2_col'

ksv-muralidhar

from sklearn.datasets import load_wine
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.DataFrame(load_wine()["data"],columns=load_wine()["feature_names"])
data.head()

moyocoyani

#Canvas
fig,ax = plt.subplots(1,1,figsize=(13,7.5),dpi=100)

#Chart
ax.plot(x,y,color=palette["primary_chart"],
        zorder=2)
ax.scatter(x,y,color=palette["background"],
           edgecolor=palette["primary"],
            s=90,
           zorder=3)