Chatting with Tesla Car Price Change Hisotry in the US

About your data

Tesla's car prices changes once in a while, this data set has the date and new prices when the changes were made.

Data Snippet

Download your data

model line name date price
0 Y SR - AWD Model Y 2023-04-06 49990
1 Y SR - AWD Model Y 2023-04-17 49990
2 Y SR - AWD Model Y 2023-04-18 46990
3 Y SR - AWD Model Y 2023-04-28 46990
4 Y SR - AWD Model Y 2023-04-30 46990

Started at June 12, 2023, 12:56 p.m.


what is the latest base line model of model 3's price
🤖
df = data.copy()
df = df[df['model'] == '3']
df = df[df['line'].isin(['SR (SR+)', 'LR-AWD', 'P'])]
df = df.sort_values('date', ascending=False)
df = df.head(1)[['model', 'name', 'date', 'price']]
df
model name date price
3 Model 3 Performance 2023-05-19 53240
no ,exclude P and LR-AWD line
🤖
df = data.copy()
df = df[df['model'] == '3']
df = df[df['line'] == 'SR (SR+)']
df = df.sort_values('date', ascending=False)
df = df.head(1)[['model', 'name', 'date', 'price']].reset_index(drop=True)
df
model name date price
3 Model 3 2023-05-19 40240
make a chart for model 3 baseline price history
🤖
df = data.copy()
df = df[(df['model'] == '3') & (df['line'] == 'SR (SR+)')]
fig = px.line(df, x='date', y='price', title='Model 3 Baseline Price History')
fig.show()
compare model x and model y prices
🤖
import numpy as np
df = data.copy()
df = df[df['model'].isin(['X', 'Y'])]
df = df.groupby(['model', 'date']).agg({'price': np.mean}).reset_index()
df
model date price
X 2018-10-01 119750.000000
X 2018-11-09 119750.000000
X 2018-11-10 119500.000000
X 2018-12-31 119500.000000
X 2019-01-01 117500.000000
X 2019-01-28 117500.000000
X 2019-01-29 116500.000000
X 2019-02-27 116500.000000
X 2019-02-28 103500.000000
X 2019-03-20 103500.000000
X 2019-03-21 106750.000000
X 2019-04-22 106750.000000
X 2019-04-23 108500.000000
X 2019-05-20 108500.000000
X 2019-05-21 106500.000000
X 2019-07-14 106500.000000
X 2019-07-15 94990.000000
X 2020-05-26 94990.000000
X 2020-05-27 89990.000000
X 2021-01-26 89990.000000
X 2021-01-27 104990.000000
X 2021-07-07 104990.000000
X 2021-07-08 107490.000000
X 2021-08-03 107490.000000
X 2021-08-04 109990.000000
X 2021-10-21 109990.000000
X 2021-10-22 112490.000000
X 2022-01-06 112490.000000
X 2022-01-07 115740.000000
X 2022-03-13 115740.000000
X 2022-03-14 126990.000000
X 2022-06-14 126990.000000
X 2022-06-15 129990.000000
X 2022-12-30 129990.000000
X 2023-01-11 129990.000000
X 2023-01-12 114990.000000
X 2023-03-04 114990.000000
X 2023-03-05 104990.000000
X 2023-04-05 104990.000000
X 2023-04-06 99990.000000
X 2023-04-19 99990.000000
X 2023-04-20 102490.000000
X 2023-05-10 102490.000000
X 2023-05-11 103490.000000
X 2023-05-19 103490.000000
Y 2019-03-15 55500.000000
Y 2019-03-23 55500.000000
Y 2019-03-24 56500.000000
Y 2020-01-28 56500.000000
Y 2020-01-29 56990.000000
Y 2020-07-10 56990.000000
Y 2020-07-11 54990.000000
Y 2021-01-07 54990.000000
Y 2021-02-16 54990.000000
Y 2021-02-17 55490.000000
Y 2021-02-20 55490.000000
Y 2021-02-21 54990.000000
Y 2021-03-10 54990.000000
Y 2021-03-11 55445.000000
Y 2021-04-07 55445.000000
Y 2021-04-08 55740.000000
Y 2021-04-21 55740.000000
Y 2021-04-22 55990.000000
Y 2021-05-05 55990.000000
Y 2021-05-06 56240.000000
Y 2021-05-20 56240.000000
Y 2021-05-21 56490.000000
Y 2021-06-08 56490.000000
Y 2021-06-09 56740.000000
Y 2021-06-24 56740.000000
Y 2021-06-25 56990.000000
Y 2021-07-21 56990.000000
Y 2021-07-22 57490.000000
Y 2021-10-04 57490.000000
Y 2021-10-05 58490.000000
Y 2021-10-21 58490.000000
Y 2021-10-22 59490.000000
Y 2021-11-03 59490.000000
Y 2021-11-04 60490.000000
Y 2021-11-10 60490.000000
Y 2021-11-11 61490.000000
Y 2022-03-08 61490.000000
Y 2022-03-09 61990.000000
Y 2022-03-13 61990.000000
Y 2022-03-14 65490.000000
Y 2022-04-09 65490.000000
Y 2022-06-14 65490.000000
Y 2022-06-15 67990.000000
Y 2022-08-20 67990.000000
Y 2022-08-21 67990.000000
Y 2022-12-01 67990.000000
Y 2022-12-22 67990.000000
Y 2023-01-11 67990.000000
Y 2023-01-12 54990.000000
Y 2023-01-22 54990.000000
Y 2023-01-23 55240.000000
Y 2023-02-02 55240.000000
Y 2023-02-03 56490.000000
Y 2023-02-09 56490.000000
Y 2023-02-10 56740.000000
Y 2023-02-11 56740.000000
Y 2023-02-12 56990.000000
Y 2023-04-05 56990.000000
Y 2023-04-06 53323.333333
Y 2023-04-17 53323.333333
Y 2023-04-18 50323.333333
Y 2023-04-28 50323.333333
Y 2023-04-30 50323.333333
Y 2023-05-01 50573.333333
Y 2023-05-10 50573.333333
Y 2023-05-11 50823.333333
Y 2023-05-19 50823.333333

112 rows × 3 columns

make a plot to compare them
🤖
import plotly.express as px

df = data.copy()
df = df[df['model'].isin(['X', 'Y'])]
df = df.groupby(['model', 'date']).agg({'price': np.mean}).reset_index()

fig = px.line(df, x='date', y='price', color='model', title='Model X vs Model Y Price Comparison')
fig.show()
latest model 3 price differnce among their lines
🤖
df = data.copy()
df = df[df['model'] == '3']
df = df.sort_values('date', ascending=False)
df = df.drop_duplicates(subset=['line'])
df = df[['line', 'name', 'date', 'price']].reset_index(drop=True)
df
line name date price
P Model 3 Performance 2023-05-19 53240
LR-AWD Model 3 Long Range 2023-05-19 47240
SR (SR+) Model 3 2023-05-19 40240

3 rows × 4 columns

latest model y price
latest model y price
🤖
df = data.copy()
df = df[df['model'] == 'Y']
df = df.sort_values('date', ascending=False)
df = df.drop_duplicates(subset=['line']).reset_index(drop=True)
df = df[['line', 'name', 'date', 'price']]
df
line name date price
P Model Y Performance 2023-05-19 54490
SR - AWD Model Y 2023-05-19 47490
LR - AWD Model Y Long Range 2023-05-19 50490
what is mean price?
🤖
df = data.copy()
mean_price = df['price'].mean()
print(pd.DataFrame([mean_price], columns=['Mean Price']))
Mean Price
70576.459732

1 rows × 1 columns

explain why the prices went down?
🤖

The prices of Tesla vehicles may have gone down due to a variety of factors. One possible reason could be improvements in production efficiency. As Tesla continues to refine its manufacturing processes, it can produce cars more cheaply and pass those savings onto customers.

Another potential reason could be changes in the cost of raw materials. If the cost of materials such as steel or aluminium falls, this could allow Tesla to reduce their prices.

Market competition could also play a role. If other manufacturers are offering similar electric vehicles at lower prices, Tesla may need to reduce its prices to stay competitive.

Lastly, government incentives and regulations could influence pricing. If governments offer incentives for electric car purchases, or impose stricter emissions regulations on gasoline cars, this could allow Tesla to price their cars more competitively.

However, without specific data points or further context, it is hard to definitively say why the prices decreased.

What is the model with the most most number of price changes?
🤖
df = data.copy()
df = df['model'].value_counts().reset_index()
df.columns = ['model', 'price_changes']
df = df.sort_values('price_changes', ascending=False)
df = df.head(1)
print(df)
model price_changes
3 257

1 rows × 2 columns

Visualize the price changes of Model Y Long Range by date
🤖
df = data.copy()
df = df[(df['model'] == 'Y') & (df['name'] == 'Model Y Long Range')].sort_values('date')
fig = px.line(df, x='date', y='price', title='Price Changes of Model Y Long Range Over Time', 
              template='plotly_white')
fig.show()
Model with lowest price
🤖
df = data.copy()
df = df[df['price'] == df['price'].min()]
print(df)
model line name date price
3 SR (SR+) Model 3 2021-02-17 36990
3 SR (SR+) Model 3 2021-02-20 36990
3 SR (SR+) Model 3 2021-02-21 36990
3 SR (SR+) Model 3 2021-03-10 36990

4 rows × 5 columns

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