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pandasai.log
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2023-08-31 14:20:46 [INFO] Question: Which are the 5 happiest countries?
2023-08-31 14:20:46 [INFO] Running PandasAI with openai LLM...
2023-08-31 14:20:46 [INFO] Prompt ID: e1d67389-59ec-487e-82e1-ffeea0d2681a
2023-08-31 14:21:41 [INFO] Question: Which are the 5 happiest countries?
2023-08-31 14:21:41 [INFO] Running PandasAI with openai LLM...
2023-08-31 14:21:41 [INFO] Prompt ID: bed81c12-b2ef-4f54-b951-9e4c0bc23fb4
2023-08-31 14:22:13 [INFO]
Code generated:
```
# TODO import all the dependencies required
import pandas as pd
# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
# Code goes here (do not add comments)
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# Filter the dataframe to include only the columns 'country' and 'happiness_index'
df = dfs[0][['country', 'happiness_index']]
# Sort the dataframe by 'happiness_index' in descending order
df_sorted = df.sort_values(by='happiness_index', ascending=False)
# Select the top 5 happiest countries
top_5_happiest_countries = df_sorted.head(5)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
return {"type": "dataframe", "value": top_5_happiest_countries}
# Declare a result variable
result = analyze_data(dfs)
```
2023-08-31 14:22:13 [INFO]
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
df = dfs[0][['country', 'happiness_index']]
df_sorted = df.sort_values(by='happiness_index', ascending=False)
top_5_happiest_countries = df_sorted.head(5)
return {'type': 'dataframe', 'value': top_5_happiest_countries}
result = analyze_data(dfs)
```
2023-08-31 14:22:13 [INFO] Answer: {'type': 'dataframe', 'value': country happiness_index
6 Canada 7.23
7 Australia 7.22
1 United Kingdom 7.16
3 Germany 7.07
0 United States 6.94}
2023-08-31 14:22:13 [INFO] Executed in: 32.65394186973572s
2023-08-31 14:22:56 [INFO] Question: Plot a bar chart of gdp and happines index of each country
2023-08-31 14:22:56 [INFO] Running PandasAI with openai LLM...
2023-08-31 14:22:56 [INFO] Prompt ID: 091fa07a-9af8-4781-9c2c-6dfbd4bc61e1
2023-08-31 14:23:23 [INFO]
Code generated:
```
# TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt
# Analyze the data
# 1. Prepare: Preprocessing and cleaning data if necessary
# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
# 4. Output: return a dictionary of:
# - type (possible values "text", "number", "dataframe", "plot")
# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
def analyze_data(dfs: list[pd.DataFrame]) -> dict:
# Code goes here (do not add comments)
df = dfs[0]
# Plotting a bar chart of gdp and happiness index
fig, ax = plt.subplots()
df.plot(x='country', y=['gdp', 'happiness_index'], kind='bar', ax=ax)
ax.set_xlabel('Country')
ax.set_ylabel('Value')
ax.set_title('GDP and Happiness Index by Country')
plt.xticks(rotation=45)
# Save the chart to an image file
chart_path = 'exports/charts/temp_chart.png'
plt.savefig(chart_path)
plt.close(fig)
return {"type": "plot", "value": chart_path}
# Declare a result variable
result = analyze_data(dfs)
```
2023-08-31 14:23:23 [INFO]
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
df = dfs[0]
fig, ax = plt.subplots()
df.plot(x='country', y=['gdp', 'happiness_index'], kind='bar', ax=ax)
ax.set_xlabel('Country')
ax.set_ylabel('Value')
ax.set_title('GDP and Happiness Index by Country')
plt.xticks(rotation=45)
chart_path = 'exports/charts/temp_chart.png'
plt.savefig(chart_path)
plt.close(fig)
return {'type': 'plot', 'value': chart_path}
result = analyze_data(dfs)
```
2023-08-31 14:23:24 [WARNING] Error of executing code
2023-08-31 14:23:25 [WARNING] Failed to execute code with a correction framework [retry number: 1]
2023-08-31 14:23:25 [INFO] Failed with error: [Errno 2] No such file or directory: 'exports/charts/temp_chart.png'. Retrying
2023-08-31 14:23:54 [WARNING] Failed to execute code with a correction framework [retry number: 2]
2023-08-31 14:23:54 [INFO] Failed with error: Generated code includes import of os which is not in whitelist.. Retrying
2023-08-31 14:24:21 [INFO]
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
df = dfs[0]
fig, ax = plt.subplots()
df.plot(x='country', y=['gdp', 'happiness_index'], kind='bar', ax=ax)
ax.set_xlabel('Country')
ax.set_ylabel('Value')
ax.set_title('GDP and Happiness Index by Country')
plt.xticks(rotation=45)
chart_path = 'exports/charts/temp_chart.png'
plt.savefig(chart_path)
plt.close(fig)
return {'type': 'plot', 'value': chart_path}
result = analyze_data(dfs)
```
2023-08-31 14:24:21 [WARNING] Error of executing code