hw3_Question1

Question1

import pandas as pd
import matplotlib.pyplot as plt

1. Load the data

df = pd.read_csv('NVDA_compustat.csv')

2. Select and report on columns

all_columns = df.columns

print(f"Total number of columns in dataset: {len(all_columns)}")

Total number of columns in dataset: 981

# Keep only specified columns
columns_needed = ['conm', 'datadate', 'fyear', 'sale', 'cogs', 'xsga', 'xrd', 'ebitda']
df = df[columns_needed]
print("\nKept columns:", ', '.join(columns_needed))

Kept columns: conm, datadate, fyear, sale, cogs, xsga, xrd, ebitda

3. Analyze reporting frequency

print("\nReporting Frequency Analysis:")
df['datadate'] = pd.to_datetime(df['datadate'])
df = df.sort_values('datadate')
days_between = df['datadate'].diff().value_counts()
print("\nDays between consecutive reports:")
print(days_between)

Reporting Frequency Analysis:

Days between consecutive reports:
datadate
365 days    20
366 days     6
396 days     1
Name: count, dtype: int64

4. Retrieve 2016 sales in three ways

print("\nNVIDIA's 2016 Sales Analysis:")

# Method 1: iloc (after finding the correct row)
row_idx = df[df['fyear'] == 2016].index[0]
col_idx = df.columns.get_loc('sale')
sales_2016_iloc = df.iloc[row_idx, col_idx]
print(f"2016 sales using iloc: ${sales_2016_iloc:,.0f}")

# Method 2: Boolean indexing
sales_2016_bool = df[df['fyear'] == 2016]['sale'].values[0]
print(f"2016 sales using boolean indexing: ${sales_2016_bool:,.0f}")

# Method 3: loc with fyear as index
df_indexed = df.set_index('fyear')
sales_2016_loc = df_indexed.loc[2016, 'sale']
print(f"2016 sales using loc: ${sales_2016_loc:,.0f}")

NVIDIA's 2016 Sales Analysis:
2016 sales using iloc: $6,910
2016 sales using boolean indexing: $6,910
2016 sales using loc: $6,910

# Reset index for further analysis
df = df_indexed.reset_index()

5. Calculate and plot sales growth

df = df.sort_values('fyear')
df['sales_growth'] = (df['sale'] / df['sale'].shift(1) - 1) * 100

# Create bar chart of sales growth
plt.figure(figsize=(12, 6))
growth_data = df[df['fyear'] >= 2000]
plt.bar(growth_data['fyear'], growth_data['sales_growth'])
plt.title('NVIDIA Annual Sales Growth Rate (2000-Present)')
plt.xlabel('Fiscal Year')
plt.ylabel('Growth Rate (%)')
plt.grid(True, axis='y')
plt.xticks(growth_data['fyear'], rotation=45)

([<matplotlib.axis.XTick at 0x13aa5fd70>,
  <matplotlib.axis.XTick at 0x13aa5fc50>,
  <matplotlib.axis.XTick at 0x13aa5d9a0>,
  <matplotlib.axis.XTick at 0x13a58ae40>,
  <matplotlib.axis.XTick at 0x13aab6d80>,
  <matplotlib.axis.XTick at 0x13aab6570>,
  <matplotlib.axis.XTick at 0x13aab57c0>,
  <matplotlib.axis.XTick at 0x13aab49b0>,
  <matplotlib.axis.XTick at 0x13aab5580>,
  <matplotlib.axis.XTick at 0x13aacfc80>,
  <matplotlib.axis.XTick at 0x13aab4710>,
  <matplotlib.axis.XTick at 0x13aaceff0>,
  <matplotlib.axis.XTick at 0x13aace150>,
  <matplotlib.axis.XTick at 0x13aacd400>,
  <matplotlib.axis.XTick at 0x13aacc560>,
  <matplotlib.axis.XTick at 0x13aab71d0>,
  <matplotlib.axis.XTick at 0x13aacda30>,
  <matplotlib.axis.XTick at 0x13aaeba70>,
  <matplotlib.axis.XTick at 0x13aaeacc0>,
  <matplotlib.axis.XTick at 0x13aae9e20>,
  <matplotlib.axis.XTick at 0x13aae8fe0>,
  <matplotlib.axis.XTick at 0x13aaea720>,
  <matplotlib.axis.XTick at 0x13aae8560>,
  <matplotlib.axis.XTick at 0x13aaea6f0>],
 [Text(2000, 0, '2000'),
  Text(2001, 0, '2001'),
  Text(2002, 0, '2002'),
  Text(2003, 0, '2003'),
  Text(2004, 0, '2004'),
  Text(2005, 0, '2005'),
  Text(2006, 0, '2006'),
  Text(2007, 0, '2007'),
  Text(2008, 0, '2008'),
  Text(2009, 0, '2009'),
  Text(2010, 0, '2010'),
  Text(2011, 0, '2011'),
  Text(2012, 0, '2012'),
  Text(2013, 0, '2013'),
  Text(2014, 0, '2014'),
  Text(2015, 0, '2015'),
  Text(2016, 0, '2016'),
  Text(2017, 0, '2017'),
  Text(2018, 0, '2018'),
  Text(2019, 0, '2019'),
  Text(2020, 0, '2020'),
  Text(2021, 0, '2021'),
  Text(2022, 0, '2022'),
  Text(2023, 0, '2023')])

6. Find min and max growth years

growth_subset = df[df['fyear'] >= 2000]
min_growth_year = growth_subset.loc[growth_subset['sales_growth'].idxmin()]
max_growth_year = growth_subset.loc[growth_subset['sales_growth'].idxmax()]

print("\nSales Growth Analysis:")
print(f"Lowest growth: {min_growth_year['fyear']} ({min_growth_year['sales_growth']:.1f}%)")
print(f"Highest growth: {max_growth_year['fyear']} ({max_growth_year['sales_growth']:.1f}%)")

Sales Growth Analysis:
Lowest growth: 2008 (-16.4%)
Highest growth: 2023 (125.9%)

7. Calculate gross profit

df['gross_profit'] = df['sale'] - df['cogs']
recent_year = df['fyear'].max()
recent_gross_profit = df[df['fyear'] == recent_year]['gross_profit'].values[0]
print(f"\nGross Profit (most recent year): ${recent_gross_profit:,.0f}")

Gross Profit (most recent year): $45,809

8. Calculate and verify EBITDA

df['calculated_ebitda'] = df['sale'] - df['cogs'] - df['xsga']
ebitda_match = (df['calculated_ebitda'] - df['ebitda']).abs().mean() < 1
print("\nEBITDA Verification:")
print(f"EBITDA calculation matches dataset: {ebitda_match}")

EBITDA Verification:
EBITDA calculation matches dataset: True

9. Calculate R&D ratio

df['rd_ratio'] = df['xrd'] / df['gross_profit'] * 100
avg_rd_ratio = df['rd_ratio'].mean()
print(f"\nR&D Analysis:")
print(f"Average R&D as % of gross profit: {avg_rd_ratio:.1f}%")

R&D Analysis:
Average R&D as % of gross profit: 47.4%

plt.tight_layout()
plt.show()

<Figure size 640x480 with 0 Axes>

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