In [1]: import pandas as pd

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import ticker
import numpy_financial as npf
import yfinance as yf
import warnings
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from datetime import datetime

In [2]: class Stock:

def __init__(self, ticker):
self.ticker = ticker
def get_stock(self,ticker):
stock = yf.Ticker(ticker).history(period="Max")
return stock

In [3]: HDFCBANK = Stock("HDFCBANK.NS").get_stock("HDFCBANK.NS")

In [4]: HDFCBANK.tail()

Out[4]:

In [5]: x = HDFCBANK.index

In [6]: y = HDFCBANK.Close
In [7]: #Plot class will plot graph through taking two argument x and y
#These instance variables represent the data that will be plotted on the line plot.
class Plot:
def __init__(self,x,y):
self.x = x
self.y = y
def line_plot(self, x,y):
from matplotlib.ticker import FuncFormatter
plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.plot(x,y, color="royalblue", alpha=0.9)
plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, _: '{:.0f}'.format(x)))
ax.grid(color='white', alpha=0.20)
plt.show()
def bar_plot(self, x,y):
from matplotlib.ticker import FuncFormatter
plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar(x,y, color="royalblue", alpha=0.9)
plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, _: '{:.0f}'.format(x)))
ax.grid(color='white', alpha=0.20)
plt.show()

In [8]: my_plot = Plot(x,y).line_plot(x[6654:],y[6654:])

my_plot = Plot(x,y).line_plot(x = HDFCBANK.index[6654:], y = HDFCBANK.Volume[6654:])

In [9]: class StockMovingAverageShort:

def __init__(self, ticker):
self.ticker = ticker

def moving_average(self, ticker):

stock = yf.Ticker(ticker).history(period="max")
stock['Price_shift_1'] = stock['Close'].shift(-1)
stock['Price_Difference'] = stock["Price_shift_1"] - stock['Close']
stock['Return'] = stock['Price_Difference'] / stock['Close']
stock['Direction'] = np.where(stock['Price_Difference'] > 0, 1, -1)
stock['3-Day_Moving_Average'] = (stock['Close'] + stock['Close'].shift(1) + stock['Close'].shift(2))/3
stock['150-Day_Moving_Average'] = stock['Close'].rolling(150).mean()
 stock['200-Day_Moving_Average'] = stock['Close'].rolling(200).mean()
return stock

In [10]: obj = StockMovingAverageShort("HDFCBANK.NS").moving_average("HDFCBANK.NS")

In [11]: fig, ax = plt.subplots(figsize=(12, 6))

obj['Close'].tail(200).plot(label='Close', ax=ax, color="white")
obj['3-Day_Moving_Average'].tail(200).plot(label='MA3', ax=ax, color="pink")
obj['150-Day_Moving_Average'].tail(200).plot(label='MA150', ax=ax, color="red")
obj['200-Day_Moving_Average'].tail(200).plot(label='MA200', ax=ax, color="green")
ax.set_xlabel('Date')
ax.set_ylabel('Price')
ax.set_title('HDFCBANK')
ax.legend()
ax.grid(color='white', alpha=0.20)
plt.tight_layout()
plt.show()

In [12]: fig, ax = plt.subplots(figsize=(12, 6))

obj.Return.plot(color="orange", alpha=0.80)
ax.set_xlabel('Date')
ax.set_title('HDFCBANK')
ax.legend()
ax.grid(color='white', alpha=0.20)
plt.tight_layout()
plt.show()
In [13]: HDFCBANK = yf.Ticker("HDFCBANK.NS")
data = HDFCBANK.history(period="5y")

In [14]: data['Date_ordinal'] = [datetime.toordinal(d) for d in data.index]

X_train, X_test, y_train, y_test = train_test_split(data['Date_ordinal'].values.reshape(-1, 1), data['Close'], test

In [15]: # Create a linear regression model

model = LinearRegression()

In [16]: # Train the model

model.fit(X_train, y_train)

Out[16]: ▾ LinearRegression

LinearRegression()

In [17]: # Predict the values

y_pred = model.predict(X_test)

In [18]: X_test = [datetime.fromordinal(int(d)) for d in X_test.flatten()]

In [19]: model.intercept_

Out[19]: -267862.43904379325

In [20]: model.coef_

Out[20]: array([0.36482439])

In [21]: plt.style.use('dark_background')
plt.figure(figsize=(30,15))
sns.scatterplot(x=X_test, y=y_test, label='Actual', color='blue')
plt.plot(X_test, y_pred, label='Predicted', color='red')
plt.title('Linear Regression Model - 3 year chart', fontsize=30, color="White", fontweight='bold')
plt.xlabel("YEAR", fontsize=20, fontweight='bold')
plt.ylabel("HDFCBANK (₹)", fontsize=20, fontweight='bold')
plt.tick_params(axis="both", labelsize=16)
plt.grid(linestyle='--', color='gray', alpha=0.7)
plt.show()

In [22]: hdfcbank_income_statement = pd.read_csv('HDFCBANK_INCOME STATEMENT .csv')

In [23]: hdfcbank_income_statement.columns.values[0] = "breakdown"

In [24]: hdfcbank_income_statement
Out[24]:

In [25]: data = hdfcbank_income_statement.loc[0]

data

Out[25]: breakdown Total Revenue

2022 1019413700
2021 936662200.0
2020 791746400.0
Name: 0, dtype: object

In [26]: year = []
revenue = []
for k, v in data.items():
if k != 'breakdown': # skip the header
year.append(int(k))
revenue.append(int(v))
year = pd.DataFrame(year)
revenue = pd.DataFrame(revenue)

In [27]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar(year[0],revenue[0], color="royalblue")
plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK Total Revenue by Year")
plt.xlabel("Year")
plt.ylabel("Revenue (₹)")
ax.grid(color='white', alpha=0.20)
plt.show()
In [28]: data = hdfcbank_income_statement.loc[4]
data = data.head(10)
data

Out[28]: breakdown Tax Provision

2022 132559200
2021 113820100.0
2020 105480000.0
Name: 4, dtype: object

In [29]: year = []
revenue = []
for k, v in data.items():
if k != 'breakdown': # skip the header
year.append(int(k))
revenue.append(int(v))
year = pd.DataFrame(year)
revenue = pd.DataFrame(revenue)

In [30]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar(year[0],revenue[0], color="royalblue")
plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFC Bank Tax Provision by Year")
plt.xlabel("Year")
plt.ylabel("Tax Provision (₹)")
for i in range(len(year)):
plt.text(year.iloc[i], revenue.iloc[i], str(revenue.iloc[i][0]), ha='center', va='bottom', fontsize=9)
plt.gca().spines['bottom'].set_linewidth(1.5)
plt.gca().spines['left'].set_linewidth(1.5)
ax.grid(color='white', alpha=0.20)
plt.show()

In [31]: data2 = hdfcbank_income_statement.loc[11]

data2

Out[31]: breakdown INTEREST_INCOME_AFTER_PROVISION_FOR_LOAN_LOSS

2022 621860000
2021 529453100.0
2020 475905500.0
Name: 11, dtype: object

In [32]: year = []
revenue = []
for k, v in data.items():
if k != 'breakdown': # skip the header
year.append(int(k))
revenue.append(int(v))
year = pd.DataFrame(year)
revenue = pd.DataFrame(revenue)

In [33]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar(year[0],revenue[0], color="royalblue")
plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFC Bank INTEREST INCOME AFTER PROVISION FOR LOAN LOSS by Year")
plt.xlabel("Year")
 plt.ylabel("Interest Income (₹)")
for i in range(len(year)):
plt.text(year.iloc[i], revenue.iloc[i], str(revenue.iloc[i][0]), ha='center', va='bottom', fontsize=9)
plt.gca().spines['bottom'].set_linewidth(1.5)
plt.gca().spines['left'].set_linewidth(1.5)
ax.grid(color='white', alpha=0.20)
plt.show()

In [34]: data3 = hdfcbank_income_statement.loc[15]

data3

Out[34]: breakdown Reconciled Depreciation

2022 16816900
2021 13860200.0
2020 12800300.0
Name: 15, dtype: object

In [35]: year = []
revenue = []
for k, v in data.items():
if k != 'breakdown': # skip the header
year.append(int(k))
revenue.append(int(v))
year = pd.DataFrame(year)
revenue = pd.DataFrame(revenue)

In [36]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar(year[0],revenue[0], color="royalblue")
plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFC BANK Reconciled Depreciation by Year")
plt.xlabel("Year")
plt.ylabel("Reconciled Depreciation (₹)")
ax.grid(color='white', alpha=0.20)
plt.show()
In [37]: data4 = hdfcbank_income_statement.loc[16]
data4

Out[37]: breakdown Net Income from Continuing Operation Net Minor...

2022 386000400
2021 325977100.0
2020 260269900.0
Name: 16, dtype: object

In [38]: year = []
revenue = []
for k, v in data.items():
if k != 'breakdown': # skip the header
year.append(int(k))
revenue.append(int(v))
year = pd.DataFrame(year)
revenue = pd.DataFrame(revenue)

In [39]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar(year[0],revenue[0], color="royalblue")
plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFC BANK Net Income from Continuing Operation Net Minority Interest by Year")
plt.xlabel("Year")
plt.ylabel("Net Income (₹)")
ax.grid(color='white', alpha=0.20)
plt.show()

In [40]: balance_sheet_annually = pd.read_csv('HDFCBANK BALANCE_SHEET.csv')

balance_sheet_annually.columns.values[0] = "breakdown"
balance_sheet_annually
Out[40]:

In [41]: total_assets = balance_sheet_annually.loc[0]

total_assets

Out[41]: breakdown Total Assets

2022 21113705500
2021 17979782000
2020 15961889100
Name: 0, dtype: object

In [42]: year = []
cash = []

for k, v in total_assets.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_total_assets = pd.DataFrame({'year': year, 'cash': cash})

In [43]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_total_assets.year, df_total_assets.cash, color="royalblue")
plt.plot( df_total_assets.year, df_total_assets.cash, color="red", linestyle="--")
plt.scatter( df_total_assets.year, df_total_assets.cash, color="red", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.title("TOTAL ASSETS BY YEAR")
plt.xlabel("Year")
plt.ylabel("Total Assets (₹)")
ax.grid(color='white', alpha=0.20)
plt.show()
In [44]: Total_Liabilities_Net_Minority_Interests = balance_sheet_annually.loc[1]
Total_Liabilities_Net_Minority_Interests

Out[44]: breakdown Total Liabilities Net Minority Interest

2022 18604252100
2021 15816377200
2020 14065395300
Name: 1, dtype: object

In [45]: year = []
cash = []

for k, v in Total_Liabilities_Net_Minority_Interests.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_Total_Liabilities_Net_Minority_Interests = pd.DataFrame({'year': year, 'cash': cash})

In [46]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_Total_Liabilities_Net_Minority_Interests.year, df_Total_Liabilities_Net_Minority_Interests.cash, color=
plt.plot( df_Total_Liabilities_Net_Minority_Interests.year, df_Total_Liabilities_Net_Minority_Interests.cash, color
plt.scatter( df_Total_Liabilities_Net_Minority_Interests.year, df_Total_Liabilities_Net_Minority_Interests.cash, co
plt.ticklabel_format(axis='y', style='plain')
plt.title("Total Liabilities Net Minority Interest")
plt.xlabel("Year")
plt.ylabel("Rupees ₹")
ax.grid(color='white', alpha=0.20)
plt.show()

In [47]: Total_Equity_Gross_Minority_Interests = balance_sheet_annually.loc[2]

Total_Equity_Gross_Minority_Interests

Out[47]: breakdown Total Equity Gross Minority Interest

2022 2509453400
2021 2163404800
2020 1896493800
Name: 2, dtype: object

In [48]: year = []
cash = []

for k, v in Total_Equity_Gross_Minority_Interests.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_Total_Equity_Gross_Minority_Interests = pd.DataFrame({'year': year, 'cash': cash})

In [49]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_Total_Equity_Gross_Minority_Interests.year, df_Total_Equity_Gross_Minority_Interests.cash, color="royal
plt.plot( df_Total_Equity_Gross_Minority_Interests.year, df_Total_Equity_Gross_Minority_Interests.cash, color="red"
plt.scatter( df_Total_Equity_Gross_Minority_Interests.year, df_Total_Equity_Gross_Minority_Interests.cash, color="r
plt.ticklabel_format(axis='y', style='plain')
plt.title("Total Equity Gross Minority Interest BY YEAR")
plt.xlabel("Year")
plt.ylabel("Rupees ₹")
 ax.grid(color='white', alpha=0.20)
plt.show()

In [50]: Total_Capitalization = balance_sheet_annually.loc[3]

Total_Capitalization

Out[50]: breakdown Total Capitalization

2022 4059171800
2021 3334386600
2020 2919600700
Name: 3, dtype: object

In [51]: year = []
cash = []

for k, v in Total_Capitalization.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_Total_Capitalization = pd.DataFrame({'year': year, 'cash': cash})

In [52]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_Total_Capitalization.year, df_Total_Capitalization.cash, color="royalblue")
plt.plot( df_Total_Capitalization.year, df_Total_Capitalization.cash, color="red", linestyle="--")
plt.scatter( df_Total_Capitalization.year, df_Total_Capitalization.cash, color="red", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.title("Total Capitalization")
plt.xlabel("Year")
plt.ylabel("Rupees ₹")
ax.grid(color='white', alpha=0.20)
plt.show()
In [53]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

#Total Assets
plt.plot(df_total_assets.year,df_total_assets.cash, color="red")
plt.scatter(df_total_assets.year,df_total_assets.cash, color="red", label="TOTAL ASSETS")

# Total Liabilities
plt.plot(df_Total_Liabilities_Net_Minority_Interests.year,df_Total_Liabilities_Net_Minority_Interests.cash, color="
plt.scatter(df_Total_Liabilities_Net_Minority_Interests.year,df_Total_Liabilities_Net_Minority_Interests.cash, colo

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK")
plt.xlabel("Year")
plt.ylabel("Rupee (₹)")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

In [54]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
#Total liabilities
plt.fill_between(df_Total_Liabilities_Net_Minority_Interests.year, df_Total_Liabilities_Net_Minority_Interests.cash
#total assets
plt.fill_between(df_total_assets.year, df_total_assets.cash, color="green", alpha=0.5, label="Total assets")
plt.ticklabel_format(axis='y', style='plain')
plt.title("CREDIT SUISSE GROUP ASSETS v/s LIABILITES")
plt.xlabel("Year")
plt.ylabel("₹")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

In [55]: Common_Stock_Equity = balance_sheet_annually.loc[4]

Common_Stock_Equity
Out[55]: breakdown Common Stock Equity
2022 2504838400
2021 2159628400
2020 1893082400
Name: 4, dtype: object

In [56]: year = []
cash = []

for k, v in Common_Stock_Equity.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_Common_Stock_Equity = pd.DataFrame({'year': year, 'cash': cash})

In [57]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_Common_Stock_Equity.year, df_Common_Stock_Equity.cash, color="royalblue")
plt.plot( df_Common_Stock_Equity.year, df_Common_Stock_Equity.cash, color="red", linestyle="--")
plt.scatter( df_Common_Stock_Equity.year, df_Common_Stock_Equity.cash, color="red", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.title("Common Stock Equity")
plt.xlabel("Year")
plt.ylabel("Total")
ax.grid(color='white', alpha=0.20)
plt.show()

In [58]: Net_Tangible_Assets = balance_sheet_annually.loc[6]

Net_Tangible_Assets

Out[58]: breakdown Net Tangible Assets

2022 2429900500
2021 2084690500
2020 1818144500
Name: 6, dtype: object

In [59]: year = []
cash = []

for k, v in Net_Tangible_Assets.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_Net_Tangible_Assets = pd.DataFrame({'year': year, 'cash': cash})

In [60]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_Net_Tangible_Assets.year, df_Net_Tangible_Assets.cash, color="royalblue")
plt.plot( df_Net_Tangible_Assets.year, df_Net_Tangible_Assets.cash, color="red", linestyle="--")
plt.scatter( df_Net_Tangible_Assets.year, df_Net_Tangible_Assets.cash, color="red", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.title("Net Tangible Assets")
plt.xlabel("Year")
plt.ylabel("Total Assets (₹)")
ax.grid(color='white', alpha=0.20)
plt.show()
In [61]: Total_Debt = balance_sheet_annually.loc[9]
Net_Debt = balance_sheet_annually.loc[10]
print(Total_Debt)
print(Net_Debt)

breakdown Total Debt

2022 2186639000
2021 1484444300
2020 1469551000
Name: 9, dtype: object
breakdown Net Debt
2022 509280300
2021 372639500
2020 677191800
Name: 10, dtype: object

In [62]: year = []
cash = []

for k, v in Total_Debt.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_Total_Debt = pd.DataFrame({'year': year, 'cash': cash})

In [63]: year = []
cash = []
for k, v in Net_Debt.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

In [64]: df_Net_Debt = pd.DataFrame({'year': year, 'cash': cash})

In [65]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

#Total Assets
plt.plot(df_Total_Debt.year,df_Total_Debt.cash, color="red")
plt.scatter(df_Total_Debt.year,df_Total_Debt.cash, color="red", label="TOTAL DEBT")

# Total Liabilities
plt.plot(df_Net_Debt.year,df_Net_Debt.cash, color="blue")
plt.scatter(df_Net_Debt.year,df_Net_Debt.cash, color="blue", label="NET DEBT")

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK")
plt.xlabel("Year")
plt.ylabel("Rupee")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()
In [66]: ## pd.set_option('display.max_rows', 100)
cashflow_annually = pd.read_csv('HDFCBANK_CASHFLOW STATEMENT.csv')
cashflow_annually.columns.values[0] = "breakdown"
cashflow_annually

Out[66]:

In [67]: operating_cash_flow_income = cashflow_annually.loc[0]

operating_cash_flow_income

Out[67]: breakdown Operating Cash Flow

2022 581018200
2021 921776200
2020 171179700
Name: 0, dtype: object

In [68]: year = []
cash = []

for k, v in operating_cash_flow_income.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_operating_cash_flow_income = pd.DataFrame({'year': year, 'cash': cash})

In [69]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

# Total Liabilities
plt.plot(df_operating_cash_flow_income.year,df_operating_cash_flow_income.cash, color="red")
plt.scatter(df_operating_cash_flow_income.year,df_operating_cash_flow_income.cash, color="white", label="Operating

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK Operating Cash Flow")
 ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_operating_cash_flow_income.year, df_operating_cash_flow_income.cash, color="darkgrey")
plt.plot( df_operating_cash_flow_income.year, df_operating_cash_flow_income.cash, color="red", linestyle="--")
plt.scatter( df_operating_cash_flow_income.year, df_operating_cash_flow_income.cash, color="white", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.xlabel("Year")
ax.grid(color='white', alpha=0.20)
plt.show()

In [70]: investing_cashflow = cashflow_annually.loc[32]

investing_cashflow

Out[70]: breakdown Investing Cash Flow

2022 -3302279800
2021 -2521149700
2020 -2150488400
Name: 32, dtype: object

In [71]: year = []
cash = []

for k, v in investing_cashflow.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_investing_cashflow = pd.DataFrame({'year': year, 'cash': cash})

In [72]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

# Total Liabilities
plt.plot(df_investing_cashflow.year,df_investing_cashflow.cash, color="red")
plt.scatter(df_investing_cashflow.year,df_investing_cashflow.cash, color="white", label="Investing Cash Flow")
 plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK Investing Cash Flow by Year")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_investing_cashflow.year, df_investing_cashflow.cash, color="darkgrey")
plt.plot( df_investing_cashflow.year, df_investing_cashflow.cash, color="red", linestyle="--")
plt.scatter( df_investing_cashflow.year, df_investing_cashflow.cash, color="white", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.xlabel("Year")
ax.grid(color='white', alpha=0.20)
plt.show()

In [73]: financing_cashflow = cashflow_annually.loc[45]

financing_cashflow

Out[73]: breakdown Financing Cash Flow

2022 2912107500
2021 1918369400
2020 1845786600
Name: 45, dtype: object

In [74]: year = []
cash = []

for k, v in financing_cashflow.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_financing_cashflow = pd.DataFrame({'year': year, 'cash': cash})

In [75]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
 # Total Liabilities
plt.plot(df_financing_cashflow.year,df_financing_cashflow.cash, color="red")
plt.scatter(df_financing_cashflow.year,df_financing_cashflow.cash, color="white", label="FINANCING CASHFLOW")

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK Financing Cash Flow by Year")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_financing_cashflow.year, df_financing_cashflow.cash, color="darkgrey")
plt.plot( df_financing_cashflow.year, df_financing_cashflow.cash, color="red", linestyle="--")
plt.scatter( df_financing_cashflow.year, df_financing_cashflow.cash, color="white", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.xlabel("Year")
ax.grid(color='white', alpha=0.20)
plt.show()

In [76]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

#Total Assets
plt.plot(df_operating_cash_flow_income.year,df_operating_cash_flow_income.cash, color="red")
plt.scatter(df_operating_cash_flow_income.year,df_operating_cash_flow_income.cash, color="red", label="Operating Ca

# Total Liabilities
plt.plot(df_investing_cashflow.year,df_investing_cashflow.cash, color="blue")
plt.scatter(df_investing_cashflow.year,df_investing_cashflow.cash, color="blue", label="Investing Cashflow")

# Total Liabilities
plt.plot(df_financing_cashflow.year,df_financing_cashflow.cash, color="darkorange")
plt.scatter(df_financing_cashflow.year,df_financing_cashflow.cash, color="darkorange", label="Financing Cashflow")

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK")
plt.xlabel("Year")
plt.ylabel("Rupee")
 ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

In [77]: change_working_capital = cashflow_annually.loc[19]

change_working_capital

Out[77]: breakdown Change in working capital

2022 58907300
2021 472346000
2020 -230662300
Name: 19, dtype: object

In [78]: year = []
cash = []

for k, v in change_working_capital.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_change_working_capital = pd.DataFrame({'year': year, 'cash': cash})

In [79]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

# Total Liabilities
plt.plot(df_change_working_capital.year,df_change_working_capital.cash, color="red")
plt.scatter(df_change_working_capital.year,df_change_working_capital.cash, color="white", label="Change in working

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK Financing Cash Flow by Year")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()
In [80]: cash_dividends_paid = cashflow_annually.loc[54]
cash_dividends_paid

Out[80]: breakdown Cash Dividends Paid

2022 -36239200
2021 -166600
2020 -66447300
Name: 54, dtype: object

In [81]: year = []
cash = []

for k, v in cash_dividends_paid.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_cash_dividends_paid = pd.DataFrame({'year': year, 'cash': cash})

In [82]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

# Total Liabilities
plt.plot(df_cash_dividends_paid.year,df_cash_dividends_paid.cash, color="red")
plt.scatter(df_cash_dividends_paid.year,df_cash_dividends_paid.cash, color="white", label="Cash Dividends Paid")

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK Cash Dividends Paid by Year")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

In [83]: changes_in_cash = cashflow_annually.loc[59]

changes_in_cash

Out[83]: breakdown Changes in Cash

2022 190845900
2021 318995900
2020 -133522100
Name: 59, dtype: object

In [84]: year = []
cash = []

for k, v in changes_in_cash.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_changes_in_cash = pd.DataFrame({'year': year, 'cash': cash})

In [85]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_changes_in_cash.year, df_changes_in_cash.cash, color="darkgrey")
plt.plot( df_changes_in_cash.year, df_changes_in_cash.cash, color="red", linestyle="--")
plt.scatter( df_changes_in_cash.year, df_changes_in_cash.cash, color="white", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
 plt.title("HDFCBANK Changes In Cash by Year")
plt.xlabel("Year")
ax.grid(color='white', alpha=0.20)
plt.show()

In [86]: cash_At_start_Of_Period = cashflow_annually.iloc[61]

cash_At_start_Of_Period

Out[86]: breakdown Beginning Cash Position

2022 930694700
2021 611961000
2020 734872600
Name: 61, dtype: object

In [87]: year = []
cash = []

for k, v in cash_At_start_Of_Period.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_cash_At_start_Of_Period = pd.DataFrame({'year': year, 'cash': cash})

In [88]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

# Total Liabilities
plt.plot(df_cash_At_start_Of_Period.year,df_cash_At_start_Of_Period.cash, color="red")
plt.scatter(df_cash_At_start_Of_Period.year,df_cash_At_start_Of_Period.cash, color="white", label="Beginning Cash P

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK Beginning Cash Position by Year")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_cash_At_start_Of_Period.year, df_cash_At_start_Of_Period.cash, color="darkgrey")
plt.plot( df_cash_At_start_Of_Period.year, df_cash_At_start_Of_Period.cash, color="red", linestyle="--")
plt.scatter( df_cash_At_start_Of_Period.year, df_cash_At_start_Of_Period.cash, color="white", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.xlabel("Year")
ax.grid(color='white', alpha=0.20)
plt.show()
In [89]: cash_At_End_Of_Period = cashflow_annually.iloc[58]
cash_At_End_Of_Period

Out[89]: breakdown End Cash Position

2022 1122031100
2021 930694700
2020 611961000
Name: 58, dtype: object

In [90]: year = []
cash = []

for k, v in cash_At_End_Of_Period.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_cash_At_End_Of_Period = pd.DataFrame({'year': year, 'cash': cash})

In [91]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

# Total Liabilities
plt.plot(df_cash_At_End_Of_Period.year,df_cash_At_End_Of_Period.cash, color="red")
plt.scatter(df_cash_At_End_Of_Period.year,df_cash_At_End_Of_Period.cash, color="white", label="End Cash Position")

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK End Cash Position by Year")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_cash_At_End_Of_Period.year, df_cash_At_End_Of_Period.cash, color="darkgrey")
plt.plot( df_cash_At_End_Of_Period.year, df_cash_At_End_Of_Period.cash, color="red", linestyle="--")
plt.scatter( df_cash_At_End_Of_Period.year, df_cash_At_End_Of_Period.cash, color="white", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.xlabel("Year")
 ax.grid(color='white', alpha=0.20)
plt.show()

In [92]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

#Total Assets
plt.plot(df_cash_At_start_Of_Period.year,df_cash_At_start_Of_Period.cash, color="red")
plt.scatter(df_cash_At_start_Of_Period.year,df_cash_At_start_Of_Period.cash, color="red", label="Operating Cashflow

# Total Liabilities
plt.plot(df_cash_At_End_Of_Period.year,df_cash_At_End_Of_Period.cash, color="darkorange")
plt.scatter(df_cash_At_End_Of_Period.year,df_cash_At_End_Of_Period.cash, color="darkorange", label="Investing Cashf

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK")
plt.xlabel("Year")
plt.ylabel("Rupee")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()
In [93]: debtRepayment = cashflow_annually.iloc[67]
debtRepayment

Out[93]: breakdown Repayment of Debt

2022 -353325700
2021 -326285000
2020 -315209600
Name: 67, dtype: object

In [94]: year = []
cash = []

for k, v in debtRepayment.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_debtRepayment = pd.DataFrame({'year': year, 'cash': cash})

In [95]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))

# Total Liabilities
plt.plot(df_debtRepayment.year,df_debtRepayment.cash, color="red")
plt.scatter(df_debtRepayment.year,df_debtRepayment.cash, color="white", label="Debt Repayments")

plt.ticklabel_format(axis='y', style='plain')
plt.title("HDFCBANK Repayment of Debt by Year")
ax.grid(color='white', alpha=0.20)
plt.legend(loc="upper left")
plt.show()

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_debtRepayment.year, df_debtRepayment.cash, color="darkgrey")
plt.plot( df_debtRepayment.year, df_debtRepayment.cash, color="red", linestyle="--")
plt.scatter( df_debtRepayment.year, df_debtRepayment.cash, color="white", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.xlabel("Year")
ax.grid(color='white', alpha=0.20)
plt.show()
In [96]: FreeCashFlow = cashflow_annually.iloc[68]
FreeCashFlow

Out[96]: breakdown Free Cash Flow

2022 554693800
2021 903969700
2020 152885400
Name: 68, dtype: object

In [97]: year = []
cash = []

for k, v in FreeCashFlow.items():
if k != 'breakdown': # skip the header
year.append(int(k))
cash.append(int(v))

df_FreeCashFlow = pd.DataFrame({'year': year, 'cash': cash})

In [98]: plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 6))
plt.bar( df_FreeCashFlow.year, df_FreeCashFlow.cash, color="darkgrey")
plt.plot( df_FreeCashFlow.year, df_FreeCashFlow.cash, color="red", linestyle="--")
plt.scatter( df_FreeCashFlow.year, df_FreeCashFlow.cash, color="white", linestyle="--")
plt.ticklabel_format(axis='y', style='plain')
plt.xlabel("Year")
plt.title("HDFCBANK Free CashFlow by Year")
ax.grid(color='white', alpha=0.20)
plt.show()
In [ ]: