#-----------------------------------------------------------------------------------------------------------------------------
#----------------------------------------------[ Needed Libraries Import ]----------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------

import pandas as pd
import plotly.graph_objs as go
import numpy as np
import plotly.offline as py
import matplotlib.pyplot as plt

#-----------------------------------------------------------------------------------------------------------------------------
#---------------------------------------------------[ Import DataBase ]-------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------

dataTelecom=pd.read_excel('S:\\TunisieTelecom\\TelecomData.xlsx')
dataTelecom.head(100)

	STATUT	OFFRE	ANC_M	HANDSET	revenu_voix	revenu_inter	NB_JOUR_ACTIVITE_TAXE	NB_JOUR_APPEL_TAXE	DUREE_APPEL_TOT	DUREE_APPEL_TAXEE	...	FREQ_USSD_VOIX	FREQ_USSD_SMS	VOLUME_SESSION	VOLUME_SESSION_WEEKEND	REVENU_VAS	ARPU	P_revenu_data	P_revenu_voix_c	P_revenu_vas_c	id_client
0	Active	Offre30	123	2G	12.709375	1.464447	10.366042	10.658842	27.261836	2.125971	...	28.679570	146.930634	200000.000000	54.025577	1.059822	4.325398	0.136685	0.943095	0.279541	1.0
1	Active	Offre8	98	2G	3.000000	0.756078	1.330736	7.079320	19.883099	7.735475	...	28.490597	169.219363	3337.992419	24.789260	1.411803	2.677563	0.533695	0.540374	0.712010	2.0
2	Active	Offre24	90	4G	32.514156	0.681197	0.158160	4.367702	133.476368	7.681088	...	190.000000	44.403308	107082.775926	160.031496	18.195224	1.954007	0.799606	0.071368	0.376754	3.0
3	Active	Offre10	226	2G	3.821551	5.265345	4.003452	3.086766	35.053364	8.540951	...	19.412661	190.000000	128700.752169	102.668979	17.746873	2.496790	0.426526	0.314082	0.676112	4.0
4	Active	Offre1	139	2G	60.009385	1.957144	5.689241	0.403300	0.844956	12.355853	...	81.567146	6.365788	75654.384291	0.341570	7.280861	2.291381	0.319736	0.600057	0.719450	5.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	Active	Offre8	159	2G	3.000000	2.250293	21.961465	2.872333	1.826910	27.907662	...	12.954468	23.226754	32664.259485	21.276030	6.134130	0.689345	0.857627	0.392768	0.657034	96.0
96	Suspended	Offre2	145	2G	3.000000	3.473084	3.575555	0.298674	80.858548	5.349497	...	153.474792	190.000000	82909.748831	26.213769	1.504337	0.164892	0.980014	0.467268	0.150409	97.0
97	Active	Offre10	185	2G	4.531545	2.142349	3.470616	6.400014	107.330103	5.199869	...	157.487581	36.176280	141996.977868	42.321968	1.637815	1.848815	0.772398	0.407095	0.545033	98.0
98	Active	Offre15	197	3G	11.762926	3.474145	2.842419	10.629819	86.011908	0.124836	...	159.332900	77.858228	12052.670191	46.685004	1.422138	1.944146	0.152620	0.243845	0.387906	99.0
99	Suspended	Offre30	178	2G	17.606096	0.019640	1.850818	0.184009	89.864928	5.858613	...	18.339092	47.120168	134081.459601	10.649038	0.640634	2.204618	0.893336	0.293854	0.121368	100.0

100 rows × 64 columns

#-----------------------------------------------------------------------------------------------------------------------------
#------------------------------------------------[ Interactive ScatterPlot]--------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------

# Ask the user to enter the names of the x and y columns
x_col = input("Enter the name of the x column: ")
y_col = input("Enter the name of the y column: ")

# Ensure that the columns exist in the DataFrame
if x_col in dataTelecom.columns and y_col in dataTelecom.columns:
    # Sort the data based on the y column (descending)
    sorted_data = dataTelecom.sort_values(by=y_col, ascending=False)

    # Create the scatter plot
    plt.figure(figsize=(10, 6))
    plt.scatter(sorted_data[x_col], sorted_data[y_col], alpha=0.5)
    plt.title(f"Scatter Plot of {x_col} vs. {y_col}")
    plt.xlabel(x_col)
    plt.ylabel(y_col)
    plt.xticks(rotation=-90)  # Rotate x-axis tick labels
    plt.grid(True)
    plt.show()
else:
    print("The specified columns do not exist in the DataFrame.")

#-----------------------------------------------------------------------------------------------------------------------------
#------------------------------------------------[ Interactive Pie Plot ]-----------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------

# Ask the user to enter the name of the column containing values
value_col = input("Write The Column Name You Wanna Visualize : ")

# Ensure that the column exists in the DataFrame
if value_col in dataTelecom.columns:
    column_data = dataTelecom[value_col]
    if pd.api.types.is_numeric_dtype(column_data):
        max_value = column_data.max()
        interval_width = max_value / 10  # Divide the range into 10 equal-width intervals
        bins = [i * interval_width for i in range(12)]
        labels = [f'{int(bins[i])}-{int(bins[i+1])}' for i in range(11)]
        labels[-1] = f'{int(bins[-2])}+'  # Replace last label with appropriate range

        # Bin the numeric values into intervals
        dataTelecom['interval'] = pd.cut(column_data, bins=bins, labels=labels, right=False)#->right=false:intervals are left-closed , right-open

        # Count the occurrences of each interval
        interval_counts = dataTelecom['interval'].value_counts()
    else:
        # For non-numeric values, keep them as they are
        dataTelecom['interval'] = column_data

        # Count the occurrences of each non-numeric value
        interval_counts = dataTelecom['interval'].value_counts()

    dist = interval_counts
    colors = ['mediumturquoise', 'darkorange']
    trace = go.Pie(values=np.array(dist), labels=dist.index)
    layout = go.Layout(title=f"Disposition Of {x_col}S")
    data = [trace]
    fig = go.Figure(data, layout)
    fig.update_traces(marker=dict(colors=colors, line=dict(color='#000000', width=1)))
    py.iplot(fig)
else:
    print("Column Name Incorrect")

#-----------------------------------------------------------------------------------------------------------------------------
#-------------------------------------------------[ Interactive Box - Plot ]--------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------

# Ask the user to enter the name of the column containing numerical values
value_col = input("Enter the name of the column with numerical values: ")

# Ensure that the column exists in the DataFrame
if value_col in dataTelecom.columns:
    # Create the box plot
    plt.figure(figsize=(8, 6))
    plt.boxplot(dataTelecom[value_col], vert=False)
    plt.title(f"Box Plot of {value_col}")
    plt.xlabel(value_col)
    plt.show()
else:
    print("The specified column does not exist in the DataFrame.")