copilot

1. Extract prices from the provided data.
2. Plot a bar graph showing the most expensive parts.
3. Provide alternative suggestions in a table format.

Python
import matplotlib.pyplot as plt

# Given part price data (part number and its highest price across distributors)
part_prices = {
    "691137710002": 0.41,
    "102010428": 5.40,
    "0603WAF2002T5E": 0.001,
    "SY6280AAAC": 0.0977,
    "AC0603FR-0715KL": 0.10,
    "0603WAF1503T5E": 0.001,
    "RT8059GJ5": 0.40,
    "GRM188R61A475KE15D": 0.0229,
    "MT3608L": 0.0398,
    "GRM32AR61C106KAB7L": 0.92,
    "GRM188R71H104KA93D": 0.0107,
    "SWPA4030S100MT": 0.17776,
    "5015": 0.244,
    "317990687": 26.1918,
    "SS34": 0.533,
    "SP3485EN-L/TR": 1.17,
    "RC0603FR-071KL": 0.0033,
    "GRM1885C1H680JA01D": 0.0254,
    "B2B-PH-K-S(LF)(SN)": 0.1501,
    "CN3063": 0.5507,
    "ETCR0603F22R0K9": 0.0041,
    "FRC0603J471 TS": 0.0008,
    "NCD0603R1": 0.007,
    "GRM219R61A226MEA0J": 0.105,
    "NCD0603C1": 0.0102,
    'RC0603FR-0768KL': 0.00382,
    'BWU.FL-IPEX1': 0.0347,
    'GRM188R60J226MEA0D': 0.027,
    'WJ103-5.0-10P': 0.5356
}

# Sort parts by price in descending order
sorted_parts = sorted(part_prices.items(), key=lambda item: item[1], reverse=True)

# Bar chart data
parts = [item[0] for item in sorted_parts[:10]]  # Top 10 most expensive parts
prices = [item[1] for item in sorted_parts[:10]]

# Plotting the bar graph
plt.figure(figsize=(14, 7))
plt.bar(parts, prices, color='blue')
plt.xlabel('Part Number')
plt.ylabel('Price ($)')
plt.title('Top 10 Most Expensive Parts in the Project')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

1. 317990687: $26.1918
2. 102010428: $5.40
3. SP3485EN-L/TR: $1.17
4. GRM32AR61C106KAB7L: $0.92
5. CN3063: $0.5507
6. WJ103-5.0-10P: $0.5356
7. SS34: $0.533
8. 691137710002: $0.41
9. RT8059GJ5: $0.40
10. 5015: $0.244

copilot

Python
import matplotlib.pyplot as plt
import pandas as pd

# Prices of ICs in the project
ic_prices = {
    "U1: RT8059GJ5": 0.14089,
    "U2: CN3063": 0.5507,
    "U3: SY6280AAAC": 0.0977,
    "U4: LoRa-E5 (STM32WLE5JC)": 26.1918,
    "U6: XIAO RP2040": 4.68,
    "IC1: MT3608L": 0.0398,
    "IC2: SP3485EN-L/TR": 0.36906,
}

# Create a pandas DataFrame
df = pd.DataFrame(list(ic_prices.items()), columns=["IC", "Price"])

# Sort the DataFrame by price in descending order
df_sorted = df.sort_values(by="Price", ascending=False)

# Plot the data
plt.figure(figsize=(10, 6))
plt.bar(df_sorted["IC"], df_sorted["Price"], color='teal')
plt.xlabel('ICs')
plt.ylabel('Price ($)')
plt.title('Prices of ICs in the Green Dot Board Project')
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.show()

copilot

1. Extract the prices for each main IC from the available stock and prices information.
2. Use Python to create a bar graph.
3. Provide recommendations for alternatives in a table format.

• XIAO RP2040 $U6$: 102010428
  • Price: $4.68
• LoRa-E5 STM32WLE5JC $U4$: 317990687
  • Price: $6.83
• RS485 Transceiver $IC2$: SP3485EN-L/TR
  • Price: $0.36906
• Buck Converter $U1$: RT8059GJ5
  • Price: $0.14089
• Power Switch $U3$: SY6280AAAC
  • Price: $0.0977
• Battery Charger IC $U2$: CN3063
  • Price: $0.5507
• Boost Converter $IC1$: MT3608L
  • Price: $0.0398

Python
import matplotlib.pyplot as plt

# Names of ICs
ic_names = ["XIAO RP2040 (U6)", "LoRa-E5 (U4)", "RS485 Transceiver (IC2)", 
            "Buck Converter (U1)", "Power Switch (U3)", 
            "Battery Charger IC (U2)", "Boost Converter (IC1)"]

# Prices of ICs
ic_prices = [4.68, 6.83, 0.36906, 0.14089, 0.0977, 0.5507, 0.0398]

# Plotting the bar graph
plt.figure(figsize=(10, 5))
plt.bar(ic_names, ic_prices, color='skyblue')
plt.xlabel("IC Names")
plt.ylabel("Prices ($)")
plt.title("Prices of Main ICs in the Project")
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

Python
import matplotlib.pyplot as plt

# Names of ICs
ic_names = ["XIAO RP2040 (U6)", "LoRa-E5 (U4)", "RS485 Transceiver (IC2)", 
            "Buck Converter (U1)", "Power Switch (U3)", 
            "Battery Charger IC (U2)", "Boost Converter (IC1)"]

# Prices of ICs
ic_prices = [4.68, 6.83, 0.36906, 0.14089, 0.0977, 0.5507, 0.0398]

# Plotting the bar graph
plt.figure(figsize=(10, 5))
plt.bar(ic_names, ic_prices, color='skyblue')
plt.xlabel("IC Names")
plt.ylabel("Prices ($)")
plt.title("Prices of Main ICs in the Project")
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

Image

copilot