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4 years ago
import pandas as pd
import numpy as np
import dtale
from pycaret.anomaly import *
from pycaret.utils import enable_colab
enable_colab()
from impyute.imputation.cs import mice
# Plots
import hvplot.pandas
from plotly.offline import init_notebook_mode, iplot
import plotly.graph_objects as go
init_notebook_mode(connected=True)
# Extras
from datetime import date
Colab mode activated. 
## Plot Pie Chart
def pie_plot(data, plot_title: str):
    trace = go.Pie(labels=data.index, values=data.values)
    layout = go.Layout(autosize=False,
    title={"text" : plot_title,
            "y" : 0.9,
            "x" : 0.5,
            "xanchor" : "center",
            "yanchor" : "top"})
    fig = go.Figure(data=trace, layout=layout)
    fig.update_traces(textfont_size=14,
                      marker=dict(line=dict(color="#000000",
                      width=2)))
    fig.update_yaxes(automargin=True)
    iplot(fig)
data = pd.read_csv('D:\Projects\credit-card-fraud-detection\data\customers_transactions.csv')
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 8950 entries, 0 to 8949 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 CUST_ID 8950 non-null object 1 BALANCE 8950 non-null float64 2 BALANCE_FREQUENCY 8950 non-null float64 3 PURCHASES 8950 non-null float64 4 ONEOFF_PURCHASES 8950 non-null float64 5 INSTALLMENTS_PURCHASES 8950 non-null float64 6 CASH_ADVANCE 8950 non-null float64 7 PURCHASES_FREQUENCY 8950 non-null float64 8 ONEOFF_PURCHASES_FREQUENCY 8950 non-null float64 9 PURCHASES_INSTALLMENTS_FREQUENCY 8950 non-null float64 10 CASH_ADVANCE_FREQUENCY 8950 non-null float64 11 CASH_ADVANCE_TRX 8950 non-null int64 12 PURCHASES_TRX 8950 non-null int64 13 CREDIT_LIMIT 8949 non-null float64 14 PAYMENTS 8950 non-null float64 15 MINIMUM_PAYMENTS 8637 non-null float64 16 PRC_FULL_PAYMENT 8950 non-null float64 17 TENURE 8950 non-null int64 dtypes: float64(14), int64(3), object(1) memory usage: 1.2+ MB 
data[data.duplicated()]