pandasai.log

2023-07-28 13:17:46 [INFO] Question: Plot a graph between Sex and Age

2023-07-28 13:17:46 [INFO] Running PandasAI with openai LLM...
2023-07-28 13:17:46 [INFO] Prompt ID: 04d1a435-f7c7-4543-93ce-ebf7f1af3451
2023-07-28 13:17:49 [INFO] 
                        Code generated:
                        ```
                        import matplotlib.pyplot as plt

df.plot(x='Sex', y='Age', kind='scatter')
plt.show()
                        ```
                    
2023-07-28 13:17:49 [INFO] 
Code running:
```
df.plot(x='Sex', y='Age', kind='scatter')
plt.show()
plt.close('all')
```
2023-07-28 13:17:57 [INFO] Answer: None
2023-07-28 13:17:57 [INFO] Executed in: 10.896167755126953s
2023-07-28 13:19:20 [INFO] Question: Plot a graph showing the correlation between GENDER and DCCI_WEIGHT
2023-07-28 13:19:20 [INFO] Running PandasAI with openai LLM...
2023-07-28 13:19:20 [INFO] Prompt ID: 827985c7-3fbb-4a9c-81f7-bea8d7d6968e
2023-07-28 13:19:29 [INFO] 
                        Code generated:
                        ```
                        import pandas as pd
import matplotlib.pyplot as plt

# Read the dataframe
df = pd.read_csv('data.csv')

# Plot the correlation between GENDER and DCCI_WEIGHT
plt.scatter(df['PATIENT_GENDER'], df['DCCI_WEIGHT'])
plt.xlabel('Gender')
plt.ylabel('DCCI Weight')
plt.title('Correlation between Gender and DCCI Weight')
plt.show()
                        ```
                    
2023-07-28 13:19:29 [INFO] 
Code running:
```
plt.scatter(df['PATIENT_GENDER'], df['DCCI_WEIGHT'])
plt.xlabel('Gender')
plt.ylabel('DCCI Weight')
plt.title('Correlation between Gender and DCCI Weight')
plt.show()
plt.close('all')
```
2023-07-28 13:19:29 [WARNING] Error executing code (count: 0)
2023-07-28 13:19:39 [WARNING] Error executing code (count: 1)
2023-07-28 13:19:46 [WARNING] Error executing code (count: 2)
2023-07-28 13:19:53 [INFO] Answer: None
2023-07-28 13:19:53 [INFO] Executed in: 33.63143873214722s
2023-07-28 13:20:35 [INFO] Question: Plot a graph showing the correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT
2023-07-28 13:20:35 [INFO] Running PandasAI with openai LLM...
2023-07-28 13:20:35 [INFO] Prompt ID: 0abf9edf-fd5b-48da-a205-9ae2431162e8
2023-07-28 13:20:52 [INFO] 
                        Code generated:
                        ```
                        import pandas as pd
import matplotlib.pyplot as plt

# Read the dataframe
df = pd.read_csv('data.csv')

# Select the columns of interest
data = df[['CPT_CATEGORY_DICT', 'DCCI_WEIGHT']]

# Drop rows with missing values
data = data.dropna()

# Convert DCCI_WEIGHT to numeric
data['DCCI_WEIGHT'] = pd.to_numeric(data['DCCI_WEIGHT'])

# Group by CPT_CATEGORY_DICT and calculate the mean of DCCI_WEIGHT
grouped_data = data.groupby('CPT_CATEGORY_DICT')['DCCI_WEIGHT'].mean().reset_index()

# Plot the correlation graph
plt.figure(figsize=(10, 6))
plt.bar(grouped_data['CPT_CATEGORY_DICT'], grouped_data['DCCI_WEIGHT'])
plt.xlabel('CPT_CATEGORY_DICT')
plt.ylabel('DCCI_WEIGHT')
plt.title('Correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT')
plt.xticks(rotation=90)
plt.show()
                        ```
                    
2023-07-28 13:20:52 [INFO] 
Code running:
```
data = df[['CPT_CATEGORY_DICT', 'DCCI_WEIGHT']]
data = data.dropna()
data['DCCI_WEIGHT'] = pd.to_numeric(data['DCCI_WEIGHT'])
grouped_data = data.groupby('CPT_CATEGORY_DICT')['DCCI_WEIGHT'].mean().reset_index()
plt.figure(figsize=(10, 6))
plt.bar(grouped_data['CPT_CATEGORY_DICT'], grouped_data['DCCI_WEIGHT'])
plt.xlabel('CPT_CATEGORY_DICT')
plt.ylabel('DCCI_WEIGHT')
plt.title('Correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT')
plt.xticks(rotation=90)
plt.show()
plt.close('all')
```
2023-07-28 13:20:56 [INFO] Answer: None
2023-07-28 13:20:56 [INFO] Executed in: 20.715823650360107s
2023-07-28 13:21:09 [INFO] Question: Plot a graph showing the correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT
2023-07-28 13:21:09 [INFO] Running PandasAI with openai LLM...
2023-07-28 13:21:09 [INFO] Prompt ID: 707b327f-bbc6-461d-be06-0d3d01e592d9
2023-07-28 13:21:09 [INFO] Using cached response
2023-07-28 13:21:09 [INFO] 
Code running:
```
data = df[['CPT_CATEGORY_DICT', 'DCCI_WEIGHT']]
data = data.dropna()
data['DCCI_WEIGHT'] = pd.to_numeric(data['DCCI_WEIGHT'])
grouped_data = data.groupby('CPT_CATEGORY_DICT')['DCCI_WEIGHT'].mean().reset_index()
plt.figure(figsize=(10, 6))
plt.bar(grouped_data['CPT_CATEGORY_DICT'], grouped_data['DCCI_WEIGHT'])
plt.xlabel('CPT_CATEGORY_DICT')
plt.ylabel('DCCI_WEIGHT')
plt.title('Correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT')
plt.xticks(rotation=90)
plt.show()
plt.close('all')
```
2023-07-28 13:21:15 [INFO] Answer: None
2023-07-28 13:21:15 [INFO] Executed in: 5.836017370223999s
2023-07-28 13:22:02 [INFO] Question: Plot a graph showing the correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT
2023-07-28 13:22:02 [INFO] Running PandasAI with openai LLM...
2023-07-28 13:22:02 [INFO] Prompt ID: c9c618e7-621e-42fe-8be7-d72fee97adb6
2023-07-28 13:22:02 [INFO] Using cached response
2023-07-28 13:22:02 [INFO] 
Code running:
```
data = df[['CPT_CATEGORY_DICT', 'DCCI_WEIGHT']]
data = data.dropna()
data['DCCI_WEIGHT'] = pd.to_numeric(data['DCCI_WEIGHT'])
grouped_data = data.groupby('CPT_CATEGORY_DICT')['DCCI_WEIGHT'].mean().reset_index()
plt.figure(figsize=(10, 6))
plt.bar(grouped_data['CPT_CATEGORY_DICT'], grouped_data['DCCI_WEIGHT'])
plt.xlabel('CPT_CATEGORY_DICT')
plt.ylabel('DCCI_WEIGHT')
plt.title('Correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT')
plt.xticks(rotation=90)
plt.show()
plt.close('all')
```
2023-07-28 13:22:06 [INFO] Answer: None
2023-07-28 13:22:06 [INFO] Executed in: 4.754411935806274s
2023-07-28 13:23:00 [INFO] Question: Plot a graph showing the correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT
2023-07-28 13:23:00 [INFO] Running PandasAI with openai LLM...
2023-07-28 13:23:00 [INFO] Prompt ID: d00e15d6-cd16-418f-9c47-ca188c5ba6f5
2023-07-28 13:23:00 [INFO] Using cached response
2023-07-28 13:23:01 [INFO] 
Code running:
```
data = df[['CPT_CATEGORY_DICT', 'DCCI_WEIGHT']]
data = data.dropna()
data['DCCI_WEIGHT'] = pd.to_numeric(data['DCCI_WEIGHT'])
grouped_data = data.groupby('CPT_CATEGORY_DICT')['DCCI_WEIGHT'].mean().reset_index()
plt.figure(figsize=(10, 6))
plt.bar(grouped_data['CPT_CATEGORY_DICT'], grouped_data['DCCI_WEIGHT'])
plt.xlabel('CPT_CATEGORY_DICT')
plt.ylabel('DCCI_WEIGHT')
plt.title('Correlation between CPT_CATEGORY_DICT and DCCI_WEIGHT')
plt.xticks(rotation=90)
plt.show()
plt.close('all')
```
2023-07-28 13:23:11 [INFO] Answer: None
2023-07-28 13:23:11 [INFO] Executed in: 10.847387790679932s
2023-07-28 13:25:43 [INFO] Question: Save a new csv file which contains the fields claim number, unique id from the given dataset
2023-07-28 13:25:43 [INFO] Running PandasAI with openai LLM...
2023-07-28 13:25:43 [INFO] Prompt ID: 93b814ce-7680-4659-a471-2b144d329d2b
2023-07-28 13:25:46 [INFO] 
                        Code generated:
                        ```
                        df[['CLAIM_NUMBER', 'UNIQUE_ID']].to_csv('new_file.csv', index=False)
                        ```
                    
2023-07-28 13:25:46 [INFO] 
Code running:
```
df[['CLAIM_NUMBER', 'UNIQUE_ID']].to_csv('new_file.csv', index=False)
```
2023-07-28 13:25:47 [INFO] Answer: None
2023-07-28 13:25:47 [INFO] Executed in: 4.719620704650879s
2023-07-28 15:07:45 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:07:45 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:07:45 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:07:45 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:07:45 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:07:45 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:08:09 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:08:09 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:08:09 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:08:09 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:08:09 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:08:09 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:45:41 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:45:44 [INFO] Question: Randomize whole dataset and save it as csv file

2023-07-28 15:45:44 [INFO] Running PandasAI with openai LLM...
2023-07-28 15:45:44 [INFO] Prompt ID: 8890f8bb-35f9-42f0-9b23-4ac7dfacddaa
2023-07-28 15:45:50 [INFO] 
                        Code generated:
                        ```
                        import pandas as pd

# Randomize the dataframe
df_randomized = df.sample(frac=1)

# Save the randomized dataframe as a csv file
df_randomized.to_csv('randomized_data.csv', index=False)
                        ```
                    
2023-07-28 15:45:50 [INFO] 
Code running:
```
df_randomized = df.sample(frac=1)
df_randomized.to_csv('randomized_data.csv', index=False)
```
2023-07-28 15:46:04 [INFO] Answer: None
2023-07-28 15:46:04 [INFO] Executed in: 20.259100675582886s
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:49:50 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:49:53 [INFO] Question: Change all values of the dataset , keep the columns as it is  and save it as csv file

2023-07-28 15:49:53 [INFO] Running PandasAI with openai LLM...
2023-07-28 15:49:53 [INFO] Prompt ID: 6416a8e4-7841-462e-94ea-88ad7deff3b3
2023-07-28 15:49:57 [INFO] 
                        Code generated:
                        ```
                        df.to_csv('new_dataset.csv', index=False)
                        ```
                    
2023-07-28 15:49:57 [INFO] 
Code running:
```
df.to_csv('new_dataset.csv', index=False)
```
2023-07-28 15:50:11 [INFO] Answer: None
2023-07-28 15:50:11 [INFO] Executed in: 18.053418159484863s
2023-07-28 15:51:57 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:51:57 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:51:57 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:51:57 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:51:57 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:51:57 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:52:41 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:52:41 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:52:41 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:52:41 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:52:41 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:52:41 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:52:45 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 15:52:45 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 15:52:45 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 15:52:45 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 15:52:45 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 15:52:45 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 15:52:47 [INFO] Question: Randomize all values of the dataset column wise  and save it as csv file

2023-07-28 15:52:47 [INFO] Running PandasAI with openai LLM...
2023-07-28 15:52:47 [INFO] Prompt ID: f697d9a9-051e-43ca-b37c-4d6e9478bb49
2023-07-28 15:52:52 [INFO] 
                        Code generated:
                        ```
                        import pandas as pd
import numpy as np

# Randomize the values of the dataframe column-wise
df_randomized = df.apply(np.random.permutation)

# Save the randomized dataframe as a csv file
df_randomized.to_csv('randomized_data.csv', index=False)
                        ```
                    
2023-07-28 15:52:52 [INFO] 
Code running:
```
df_randomized = df.apply(np.random.permutation)
df_randomized.to_csv('randomized_data.csv', index=False)
```
2023-07-28 15:53:06 [INFO] Answer: None
2023-07-28 15:53:06 [INFO] Executed in: 19.111257076263428s
2023-07-28 16:01:56 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 16:01:56 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 16:01:56 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 16:01:56 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 16:01:56 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 16:01:56 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 16:01:58 [INFO] Question: Randomize all values of the dataset column wise  and save it as csv file

2023-07-28 16:01:58 [INFO] Running PandasAI with openai LLM...
2023-07-28 16:01:58 [INFO] Prompt ID: ebc56ce8-f609-45bf-93d8-1f1af12b8279
2023-07-28 16:01:58 [INFO] Using cached response
2023-07-28 16:01:58 [INFO] 
Code running:
```
df_randomized = df.apply(np.random.permutation)
df_randomized.to_csv('randomized_data.csv', index=False)
```
2023-07-28 16:02:13 [INFO] Answer: None
2023-07-28 16:02:13 [INFO] Executed in: 14.544702768325806s
2023-07-28 16:05:05 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 16:05:05 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 16:05:05 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 16:05:05 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 16:05:05 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 16:05:05 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 16:05:07 [INFO] Question: Randomize all values of the dataset column wise  and save it as csv file

2023-07-28 16:05:07 [INFO] Running PandasAI with openai LLM...
2023-07-28 16:05:07 [INFO] Prompt ID: 3052fd94-92f2-4e7e-8aa5-e8084410ae85
2023-07-28 16:05:07 [INFO] Using cached response
2023-07-28 16:05:07 [INFO] 
Code running:
```
df_randomized = df.apply(np.random.permutation)
df_randomized.to_csv('randomized_data.csv', index=False)
```
2023-07-28 16:05:22 [INFO] Answer: None
2023-07-28 16:05:22 [INFO] Executed in: 14.167450666427612s
2023-07-28 16:06:26 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 16:06:26 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 16:06:26 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 16:06:26 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 16:06:26 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 16:06:26 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 16:06:28 [INFO] Question: Randomize all values of the dataset column wise  and save it as csv file

2023-07-28 16:06:28 [INFO] Running PandasAI with openai LLM...
2023-07-28 16:06:28 [INFO] Prompt ID: 52a7d44c-9a92-4fed-b7e1-11d310d46535
2023-07-28 16:06:28 [INFO] Using cached response
2023-07-28 16:06:28 [INFO] 
Code running:
```
df_randomized = df.apply(np.random.permutation)
df_randomized.to_csv('randomized_data.csv', index=False)
```
2023-07-28 16:06:41 [INFO] Answer: None
2023-07-28 16:06:41 [INFO] Executed in: 13.362996339797974s
2023-07-28 17:49:01 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 17:49:01 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 17:49:01 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 17:49:01 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 17:49:01 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 17:49:01 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 17:49:40 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 17:49:40 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 17:49:40 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 17:49:40 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 17:49:40 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 17:49:40 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 17:49:53 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-07-28 17:49:56 [INFO] Question: describe the data

2023-07-28 17:49:56 [INFO] Running PandasAI with openai LLM...
2023-07-28 17:49:56 [INFO] Prompt ID: 8d8d838f-dc88-4b09-96b4-659735637dbc
2023-07-28 17:50:06 [INFO] 
                        Code generated:
                        ```
                        import pandas as pd

# Read the dataframe from a csv file
df = pd.read_csv('data.csv')

# Get the shape of the dataframe
shape = df.shape
print("Shape of the dataframe:", shape)

# Get the column names of the dataframe
columns = df.columns
print("Column names:", columns)

# Get the data types of the columns
dtypes = df.dtypes
print("Data types of columns:")
print(dtypes)

# Get the summary statistics of the numerical columns
summary_stats = df.describe()
print("Summary statistics of numerical columns:")
print(summary_stats)
                        ```
                    
2023-07-28 17:50:06 [INFO] 
Code running:
```
shape = df.shape
print('Shape of the dataframe:', shape)
columns = df.columns
print('Column names:', columns)
dtypes = df.dtypes
print('Data types of columns:')
print(dtypes)
summary_stats = df.describe()
print('Summary statistics of numerical columns:')
print(summary_stats)
```
2023-07-28 17:50:06 [INFO] Answer: Shape of the dataframe: (119610, 48)
Column names: Index(['UNIQUE_ID', 'PATIENT_DOB', 'PATIENT_GENDER', 'DIAGNOSIS_DATE',
       'RECONCILED_DIAGNOSIS_CODE_ICD10', 'TOTAL_BILLED_AMT',
       'TOTAL_EMPLOYER_PAID_AMT', 'PRIMARY_PROCEDURE_CODE', 'PLACE_OF_SERVICE',
       'DEPENDENT_SSN', 'TYPE_OF_SERVICE_CODE', 'SERVICE_CODE',
       'COVERAGE_TYPE', 'ADMIT_DATE', 'DISCHARGE_DATE', 'HOSPITALIZED_OR_NOT',
       'STAGING_DATE', 'DIAGNOSIS_CODE_2', 'DIAGNOSIS_CODE_3',
       'DIAGNOSIS_CODE_4', 'PROCEDURE_CODE_2', 'PROCEDURE_CODE_3',
       'PROCEDURE_CODE_4', 'CHRONICITY_IDENTIFIER', 'CCW_CHRONIC_CAT',
       'LT_ALL_ABSENCES_MIDRANGE', 'MSK_MRS_CODE_CLASSIFICATION',
       'ICD_DESCRIPTION', 'TRUE_MSK_FLAG', 'DISEASE_CATEGORY',
       'DISEASE_CATEGORY_DICT', 'DCCI_CATEGORY', 'DCCI_WEIGHT',
       'EVCI_CATEGORY', 'EVCI_WEIGHT', 'CHRONIC_CAT_TYPE', 'AVOIDABLE_ER',
       'CATEGORY', 'CPT_CATEGORY_DICT', 'SUB_CATEGORY', 'CPT_DESCRIPTION',
       'FILE_YEAR', 'FILE_MONTH', 'FILE_MONTH_YEAR', 'HYPERTENSION_FLAG',
       'ARTHRITIS_FLAG', 'HEART_DISEASE_FLAG', 'DIABETES_FLAG'],
      dtype='object')
Data types of columns:
UNIQUE_ID                           object
PATIENT_DOB                         object
PATIENT_GENDER                      object
DIAGNOSIS_DATE                      object
RECONCILED_DIAGNOSIS_CODE_ICD10     object
TOTAL_BILLED_AMT                   float64
TOTAL_EMPLOYER_PAID_AMT            float64
PRIMARY_PROCEDURE_CODE              object
PLACE_OF_SERVICE                    object
DEPENDENT_SSN                      float64
TYPE_OF_SERVICE_CODE                object
SERVICE_CODE                        object
COVERAGE_TYPE                      float64
ADMIT_DATE                          object
DISCHARGE_DATE                      object
HOSPITALIZED_OR_NOT                 object
STAGING_DATE                        object
DIAGNOSIS_CODE_2                    object
DIAGNOSIS_CODE_3                    object
DIAGNOSIS_CODE_4                    object
PROCEDURE_CODE_2                   float64
PROCEDURE_CODE_3                   float64
PROCEDURE_CODE_4                   float64
CHRONICITY_IDENTIFIER               object
CCW_CHRONIC_CAT                     object
LT_ALL_ABSENCES_MIDRANGE           float64
MSK_MRS_CODE_CLASSIFICATION         object
ICD_DESCRIPTION                     object
TRUE_MSK_FLAG                       object
DISEASE_CATEGORY                    object
DISEASE_CATEGORY_DICT               object
DCCI_CATEGORY                       object
DCCI_WEIGHT                        float64
EVCI_CATEGORY                       object
EVCI_WEIGHT                        float64
CHRONIC_CAT_TYPE                    object
AVOIDABLE_ER                        object
CATEGORY                            object
CPT_CATEGORY_DICT                   object
SUB_CATEGORY                        object
CPT_DESCRIPTION                     object
FILE_YEAR                            int64
FILE_MONTH                           int64
FILE_MONTH_YEAR                     object
HYPERTENSION_FLAG                    int64
ARTHRITIS_FLAG                       int64
HEART_DISEASE_FLAG                   int64
DIABETES_FLAG                        int64
dtype: object
Summary statistics of numerical columns:
       TOTAL_BILLED_AMT  TOTAL_EMPLOYER_PAID_AMT  DEPENDENT_SSN  ...  ARTHRITIS_FLAG  HEART_DISEASE_FLAG  DIABETES_FLAG
count      1.196100e+05             1.196100e+05  115937.000000  ...        119610.0       119610.000000  119610.000000
mean       4.503378e+02             1.911447e+02    5168.343126  ...             0.0            0.005518       0.019447
std        6.051569e+03             3.983369e+03    2947.285189  ...             0.0            0.074078       0.138089
min       -2.540280e+04             0.000000e+00       2.000000  ...             0.0            0.000000       0.000000
25%        4.799000e+01             7.610000e+00    2526.000000  ...             0.0            0.000000       0.000000
50%        1.100000e+02             3.741000e+01    5188.000000  ...             0.0            0.000000       0.000000
75%        2.199950e+02             1.100000e+02    7713.000000  ...             0.0            0.000000       0.000000
max        1.791693e+06             1.254185e+06    9980.000000  ...             0.0            1.000000       1.000000

[8 rows x 16 columns]
2023-07-28 17:50:06 [INFO] Executed in: 10.543148756027222s
2023-07-28 20:38:59 [WARNING] Python-dotenv could not parse statement starting at line 4
2023-07-28 20:38:59 [WARNING] Python-dotenv could not parse statement starting at line 6
2023-07-28 20:38:59 [WARNING] Python-dotenv could not parse statement starting at line 8
2023-07-28 20:38:59 [WARNING] Python-dotenv could not parse statement starting at line 10
2023-07-28 20:38:59 [WARNING] Python-dotenv could not parse statement starting at line 12
2023-07-28 20:38:59 [WARNING] Python-dotenv could not parse statement starting at line 14
2023-10-18 14:58:51 [INFO] Question: Delete release date column from this csv and save it to a new file as movies.csv
2023-10-18 14:58:51 [INFO] Running PandasAI with openai LLM...
2023-10-18 14:58:51 [INFO] Prompt ID: 3f99fa4c-101a-4d20-8088-0b5a40554fe2
2023-10-18 14:58:51 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
2,7/28/1995,Waterworld,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
5,6/22/2018,Jurassic World: Fallen Kingdom,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
4,12/25/2013,47 Ronin,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
3,5/12/2017,King Arthur: Legend of the Sword,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
</dataframe>

<conversation>
User 1: Delete release date column from this csv and save it to a new file as movies.csv
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-18 14:58:52 [INFO] error_code=insufficient_quota error_message='You exceeded your current quota, please check your plan and billing details.' error_param=None error_type=insufficient_quota message='OpenAI API error received' stream_error=False
2023-10-18 15:00:42 [INFO] Question: Delete release date from this csv and save it in a new file as movies.csv
2023-10-18 15:00:42 [INFO] Running PandasAI with openai LLM...
2023-10-18 15:00:42 [INFO] Prompt ID: 05b51ab4-c9e1-4701-bb95-ef9f3fd61eb6
2023-10-18 15:00:42 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
5,6/22/2018,Jurassic World: Fallen...,170000000.0,416769345.0,7342457258,Universal,PG-13,Action
4,12/25/2013,47 Ronin...,175000000.0,38362475.0,0803758673,Universal,PG-13,Action
1,6/22/2007,Evan Almighty...,175000000.0,100289690.0,5329988663,Universal,PG,Comedy
3,5/12/2017,King Arthur: Legend of...,175000000.0,39175066.0,2725337845,Warner Bros.,PG-13,Adventure
2,7/28/1995,Waterworld...,175000000.0,88246220.0,6163453335,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: Delete release date from this csv and save it in a new file as movies.csv
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-18 15:00:43 [INFO] error_code=insufficient_quota error_message='You exceeded your current quota, please check your plan and billing details.' error_param=None error_type=insufficient_quota message='OpenAI API error received' stream_error=False
2023-10-18 15:01:31 [INFO] Question: Delete release date from this csv and save it in a new file as movies.csv
2023-10-18 15:01:31 [INFO] Running PandasAI with openai LLM...
2023-10-18 15:01:31 [INFO] Prompt ID: 2db1530b-ac2d-425f-8b9a-4d08ae7c5001
2023-10-18 15:01:31 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
3,5/12/2017,King Arthur: Legend of...,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
4,12/25/2013,47 Ronin...,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
5,6/22/2018,Jurassic World: Fallen...,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty...,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
2,7/28/1995,Waterworld...,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: Delete release date from this csv and save it in a new file as movies.csv
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-18 15:01:32 [INFO] error_code=insufficient_quota error_message='You exceeded your current quota, please check your plan and billing details.' error_param=None error_type=insufficient_quota message='OpenAI API error received' stream_error=False
2023-10-18 15:11:50 [INFO] Question: Delete release date from this csv and save it in a new file as movies.csv
2023-10-18 15:11:50 [INFO] Running PandasAI with openai LLM...
2023-10-18 15:11:50 [INFO] Prompt ID: 75b3b495-3113-414a-81f2-580576a3c918
2023-10-18 15:11:50 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
5,6/22/2018,Jurassic World: Fallen...,170000000.0,416769345.0,0272167540,Universal,PG-13,Action
2,7/28/1995,Waterworld...,175000000.0,88246220.0,6874447137,Universal,PG-13,Action
3,5/12/2017,King Arthur: Legend of...,175000000.0,39175066.0,1613627724,Warner Bros.,PG-13,Adventure
4,12/25/2013,47 Ronin...,175000000.0,38362475.0,5663333377,Universal,PG-13,Action
1,6/22/2007,Evan Almighty...,175000000.0,100289690.0,5466587799,Universal,PG,Comedy
</dataframe>

<conversation>
User 1: Delete release date from this csv and save it in a new file as movies.csv
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-18 15:11:51 [INFO] error_code=insufficient_quota error_message='You exceeded your current quota, please check your plan and billing details.' error_param=None error_type=insufficient_quota message='OpenAI API error received' stream_error=False
2023-10-18 15:12:00 [INFO] Question: 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
2023-10-18 15:12:00 [INFO] Running PandasAI with openai LLM...
2023-10-18 15:12:00 [INFO] Prompt ID: a3543ee2-7ea3-4f4b-8797-efd3b436200a
2023-10-18 15:12:00 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
5,6/22/2018,Jurassic World: Fallen...,170000000.0,416769345.0,9325946063,Universal,PG-13,Action
4,12/25/2013,47 Ronin...,175000000.0,38362475.0,0384248965,Universal,PG-13,Action
2,7/28/1995,Waterworld...,175000000.0,88246220.0,6232271770,Universal,PG-13,Action
3,5/12/2017,King Arthur: Legend of...,175000000.0,39175066.0,4003421667,Warner Bros.,PG-13,Adventure
1,6/22/2007,Evan Almighty...,175000000.0,100289690.0,1704095860,Universal,PG,Comedy
</dataframe>

<conversation>
User 1: 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
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-18 15:12:00 [INFO] error_code=insufficient_quota error_message='You exceeded your current quota, please check your plan and billing details.' error_param=None error_type=insufficient_quota message='OpenAI API error received' stream_error=False
2023-10-18 15:16:29 [INFO] Question: delete release_date column

2023-10-18 15:16:29 [INFO] Running PandasAI with openai LLM...
2023-10-18 15:16:29 [INFO] Prompt ID: c891c568-c09e-40e4-970c-e9a7cf7e7994
2023-10-18 15:16:29 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
4,12/25/2013,47 Ronin,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
3,5/12/2017,King Arthur: Legend of the Sword,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
5,6/22/2018,Jurassic World: Fallen Kingdom,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
2,7/28/1995,Waterworld,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: delete release_date column

</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-18 15:16:55 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Preprocessing and cleaning data
    dfs[0] = dfs[0].drop(columns=['release_date'])
    
    return {"type": "dataframe", "value": dfs[0]}
```

2023-10-18 15:16:55 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    dfs[0] = dfs[0].drop(columns=['release_date'])
    return {'type': 'dataframe', 'value': dfs[0]}
        ```
2023-10-18 15:16:55 [INFO] Answer: {'type': 'dataframe', 'value':       Unnamed: 0                             movie  production_budget  ...             distributor  mpaa_rating      genre
0              1                     Evan Almighty        175000000.0  ...               Universal           PG     Comedy
1              2                        Waterworld        175000000.0  ...               Universal        PG-13     Action
2              3  King Arthur: Legend of the Sword        175000000.0  ...            Warner Bros.        PG-13  Adventure
3              4                          47 Ronin        175000000.0  ...               Universal        PG-13     Action
4              5    Jurassic World: Fallen Kingdom        170000000.0  ...               Universal        PG-13     Action
...          ...                               ...                ...  ...                     ...          ...        ...
3396        3397                        Like Crazy           250000.0  ...      Paramount Pictures        PG-13      Drama
3397        3398                        Chuck&Buck           250000.0  ...                 Artisan            R      Drama
3398        3399       Love and Other Catastrophes           250000.0  ...         Fox Searchlight            R     Comedy
3399        3400        I Married a Strange Person           250000.0  ...               Lionsgate          NaN     Comedy
3400        3401                          November           250000.0  ...  Sony Pictures Classics            R      Drama

[3401 rows x 8 columns]}
2023-10-18 15:16:55 [INFO] Executed in: 25.307244777679443s
2023-10-18 15:17:48 [INFO] Question: delete all fields in movie column and save it as new file as new.csv
2023-10-18 15:17:48 [INFO] Running PandasAI with openai LLM...
2023-10-18 15:17:48 [INFO] Prompt ID: 8a5ed285-17a7-4abf-b4c6-83fcb97d501e
2023-10-18 15:17:48 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
4,12/25/2013,47 Ronin,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
3,5/12/2017,King Arthur: Legend of the Sword,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
5,6/22/2018,Jurassic World: Fallen Kingdom,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
2,7/28/1995,Waterworld,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: delete all fields in movie column and save it as new file as new.csv
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-18 15:18:15 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Delete all fields in movie column and save it as new file as new.csv
    dfs[0].drop(columns=['movie'], inplace=True)
    dfs[0].to_csv('new.csv', index=False)
    
    return {"type": "string", "value": "Fields in movie column deleted and saved as new.csv."}
```

2023-10-18 15:18:15 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    dfs[0].drop(columns=['movie'], inplace=True)
    return {'type': 'string', 'value': 'Fields in movie column deleted and saved as new.csv.'}
        ```
2023-10-18 15:18:15 [INFO] Answer: {'type': 'string', 'value': 'Fields in movie column deleted and saved as new.csv.'}
2023-10-18 15:18:15 [INFO] Executed in: 27.455315828323364s
2023-10-18 18:53:15 [INFO] Question: change all fields in movies column to IRON MAN 2
2023-10-18 18:53:15 [INFO] Running PandasAI with openai LLM...
2023-10-18 18:53:15 [INFO] Prompt ID: 83aa5975-1d34-4394-9fbe-ee5f7135c181
2023-10-18 18:53:15 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
4,12/25/2013,47 Ronin,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
3,5/12/2017,King Arthur: Legend of the Sword,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
5,6/22/2018,Jurassic World: Fallen Kingdom,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
2,7/28/1995,Waterworld,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: change all fields in movies column to IRON MAN 2
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-18 18:53:50 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Update the movies column in the first dataframe
    dfs[0]['movie'] = 'IRON MAN 2'
    
    # Return the updated dataframe
    return {"type": "dataframe", "value": dfs[0]}
```

2023-10-18 18:53:50 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    dfs[0]['movie'] = 'IRON MAN 2'
    return {'type': 'dataframe', 'value': dfs[0]}
        ```
2023-10-18 18:53:50 [INFO] Answer: {'type': 'dataframe', 'value':       Unnamed: 0 release_date       movie  production_budget  ...  worldwide_gross             distributor mpaa_rating      genre
0              1    6/22/2007  IRON MAN 2        175000000.0  ...     1.741313e+08               Universal          PG     Comedy
1              2    7/28/1995  IRON MAN 2        175000000.0  ...     2.642462e+08               Universal       PG-13     Action
2              3    5/12/2017  IRON MAN 2        175000000.0  ...     1.399507e+08            Warner Bros.       PG-13  Adventure
3              4   12/25/2013  IRON MAN 2        175000000.0  ...     1.517168e+08               Universal       PG-13     Action
4              5    6/22/2018  IRON MAN 2        170000000.0  ...     1.304866e+09               Universal       PG-13     Action
...          ...          ...         ...                ...  ...              ...                     ...         ...        ...
3396        3397   10/28/2011  IRON MAN 2           250000.0  ...     3.728400e+06      Paramount Pictures       PG-13      Drama
3397        3398    7/14/2000  IRON MAN 2           250000.0  ...     1.157672e+06                 Artisan           R      Drama
3398        3399    3/28/1997  IRON MAN 2           250000.0  ...     7.432160e+05         Fox Searchlight           R     Comedy
3399        3400    8/28/1998  IRON MAN 2           250000.0  ...     2.031340e+05               Lionsgate         NaN     Comedy
3400        3401    7/22/2005  IRON MAN 2           250000.0  ...     1.918620e+05  Sony Pictures Classics           R      Drama

[3401 rows x 9 columns]}
2023-10-18 18:53:50 [INFO] Executed in: 35.10925507545471s
2023-10-19 14:50:05 [INFO] Question: Make a relationship graph between production budget and genre
2023-10-19 14:50:05 [INFO] Running PandasAI with openai LLM...
2023-10-19 14:50:05 [INFO] Prompt ID: 6a2397eb-0fcf-4e6d-8658-54e7df7b881e
2023-10-19 14:50:05 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
3,5/12/2017,King Arthur: Legend of...,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
4,12/25/2013,47 Ronin...,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty...,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
5,6/22/2018,Jurassic World: Fallen...,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
2,7/28/1995,Waterworld...,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: Make a relationship graph between production budget and genre
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-19 14:50:46 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Prepare
    df = dfs[0].copy()
    df['production_budget'] = df['production_budget'].astype(int)
    
    # Process
    genre_budget = df.groupby('genre')['production_budget'].sum().reset_index()
    
    # Analyze
    plt.figure(figsize=(12, 6))
    plt.bar(genre_budget['genre'], genre_budget['production_budget'])
    plt.xlabel('Genre')
    plt.ylabel('Production Budget')
    plt.title('Relationship between Production Budget and Genre')
    plt.xticks(rotation=45)
    plt.savefig('temp_chart.png')
    plt.close()
    
    return {"type": "plot", "value": "temp_chart.png"}
```

2023-10-19 14:50:46 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    df = dfs[0].copy()
    df['production_budget'] = df['production_budget'].astype(int)
    genre_budget = df.groupby('genre')['production_budget'].sum().reset_index()
    plt.figure(figsize=(12, 6))
    plt.bar(genre_budget['genre'], genre_budget['production_budget'])
    plt.xlabel('Genre')
    plt.ylabel('Production Budget')
    plt.title('Relationship between Production Budget and Genre')
    plt.xticks(rotation=45)
    plt.savefig('temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'temp_chart.png'}
        ```
2023-10-19 14:52:46 [INFO] Question: plot a graph between production budget and genre
2023-10-19 14:52:46 [INFO] Running PandasAI with openai LLM...
2023-10-19 14:52:46 [INFO] Prompt ID: 6165bbed-7a4f-4391-b3b7-a0bd1dc7bb94
2023-10-19 14:52:46 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
2,7/28/1995,Waterworld,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
4,12/25/2013,47 Ronin,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
3,5/12/2017,King Arthur: Legend of the Sword,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
5,6/22/2018,Jurassic World: Fallen Kingdom,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: plot a graph between production budget and genre
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-19 14:53:32 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Prepare
    df = dfs[0]
    
    # Process
    grouped_df = df.groupby('genre')['production_budget'].sum().reset_index()
    
    # Analyze
    plt.figure(figsize=(12, 6))
    plt.bar(grouped_df['genre'], grouped_df['production_budget'])
    plt.xlabel('Genre')
    plt.ylabel('Production Budget')
    plt.title('Production Budget by Genre')
    plt.xticks(rotation=45)
    plt.savefig('temp_chart.png')
    plt.close()
    
    return {"type": "plot", "value": "temp_chart.png"}
```

2023-10-19 14:53:32 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    df = dfs[0]
    grouped_df = df.groupby('genre')['production_budget'].sum().reset_index()
    plt.figure(figsize=(12, 6))
    plt.bar(grouped_df['genre'], grouped_df['production_budget'])
    plt.xlabel('Genre')
    plt.ylabel('Production Budget')
    plt.title('Production Budget by Genre')
    plt.xticks(rotation=45)
    plt.savefig('temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'temp_chart.png'}
        ```
2023-10-19 14:55:11 [INFO] Question: remove movie column
2023-10-19 14:55:11 [INFO] Running PandasAI with openai LLM...
2023-10-19 14:55:11 [INFO] Prompt ID: e66faaac-d62a-4d4d-8b5f-8c13a16c18c6
2023-10-19 14:55:11 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
1,6/22/2007,Evan Almighty,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
4,12/25/2013,47 Ronin,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
5,6/22/2018,Jurassic World: Fallen Kingdom,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
2,7/28/1995,Waterworld,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
3,5/12/2017,King Arthur: Legend of the Sword,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
</dataframe>

<conversation>
User 1: remove movie column
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-19 14:55:49 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Remove the 'movie' column from the first dataframe
    dfs[0] = dfs[0].drop(columns=['movie'])
    
    return {"type": "dataframe", "value": dfs[0]}
```

2023-10-19 14:55:49 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    dfs[0] = dfs[0].drop(columns=['movie'])
    return {'type': 'dataframe', 'value': dfs[0]}
        ```
2023-10-19 14:55:49 [INFO] Answer: {'type': 'dataframe', 'value':       Unnamed: 0 release_date  production_budget  domestic_gross  worldwide_gross             distributor mpaa_rating      genre
0              1    6/22/2007        175000000.0     100289690.0     1.741313e+08               Universal          PG     Comedy
1              2    7/28/1995        175000000.0      88246220.0     2.642462e+08               Universal       PG-13     Action
2              3    5/12/2017        175000000.0      39175066.0     1.399507e+08            Warner Bros.       PG-13  Adventure
3              4   12/25/2013        175000000.0      38362475.0     1.517168e+08               Universal       PG-13     Action
4              5    6/22/2018        170000000.0     416769345.0     1.304866e+09               Universal       PG-13     Action
...          ...          ...                ...             ...              ...                     ...         ...        ...
3396        3397   10/28/2011           250000.0       3395391.0     3.728400e+06      Paramount Pictures       PG-13      Drama
3397        3398    7/14/2000           250000.0       1055671.0     1.157672e+06                 Artisan           R      Drama
3398        3399    3/28/1997           250000.0        212285.0     7.432160e+05         Fox Searchlight           R     Comedy
3399        3400    8/28/1998           250000.0        203134.0     2.031340e+05               Lionsgate         NaN     Comedy
3400        3401    7/22/2005           250000.0        191862.0     1.918620e+05  Sony Pictures Classics           R      Drama

[3401 rows x 8 columns]}
2023-10-19 14:55:49 [INFO] Executed in: 38.55382800102234s
2023-10-19 14:56:39 [INFO] Question: plot a graph between production budget and genre
2023-10-19 14:56:39 [INFO] Running PandasAI with openai LLM...
2023-10-19 14:56:39 [INFO] Prompt ID: a2dd5183-c2db-4475-8013-8b9d1b1fcff4
2023-10-19 14:56:39 [INFO] Using cached response
2023-10-19 14:56:39 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Prepare
    df = dfs[0]
    
    # Process
    grouped_df = df.groupby('genre')['production_budget'].sum().reset_index()
    
    # Analyze
    plt.figure(figsize=(12, 6))
    plt.bar(grouped_df['genre'], grouped_df['production_budget'])
    plt.xlabel('Genre')
    plt.ylabel('Production Budget')
    plt.title('Production Budget by Genre')
    plt.xticks(rotation=45)
    plt.savefig('temp_chart.png')
    plt.close()
    
    return {"type": "plot", "value": "temp_chart.png"}
```

2023-10-19 14:56:39 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    df = dfs[0]
    grouped_df = df.groupby('genre')['production_budget'].sum().reset_index()
    plt.figure(figsize=(12, 6))
    plt.bar(grouped_df['genre'], grouped_df['production_budget'])
    plt.xlabel('Genre')
    plt.ylabel('Production Budget')
    plt.title('Production Budget by Genre')
    plt.xticks(rotation=45)
    plt.savefig('temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'temp_chart.png'}
        ```
2023-10-19 14:57:49 [INFO] Question: plot a graph between distributor and genre
2023-10-19 14:57:49 [INFO] Running PandasAI with openai LLM...
2023-10-19 14:57:49 [INFO] Prompt ID: 1fce90c7-6e54-42ca-b3d0-fe35b1ff8c82
2023-10-19 14:57:49 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
5,6/22/2018,Jurassic World: Fallen...,170000000.0,416769345.0,3444461824,Universal,PG-13,Action
1,6/22/2007,Evan Almighty...,175000000.0,100289690.0,1403565918,Universal,PG,Comedy
2,7/28/1995,Waterworld...,175000000.0,88246220.0,6924773766,Universal,PG-13,Action
3,5/12/2017,King Arthur: Legend of...,175000000.0,39175066.0,3646828244,Warner Bros.,PG-13,Adventure
4,12/25/2013,47 Ronin...,175000000.0,38362475.0,0717765359,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: plot a graph between distributor and genre
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-19 14:58:33 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Prepare
    df = dfs[0]
    
    # Process
    grouped_df = df.groupby(['distributor', 'genre']).size().unstack()
    
    # Analyze
    plt.figure(figsize=(12, 6))
    grouped_df.plot(kind='bar')
    plt.xlabel('Distributor')
    plt.ylabel('Count')
    plt.title('Number of Movies by Distributor and Genre')
    plt.legend(title='Genre')
    plt.savefig('temp_chart.png')
    plt.close()
    
    return {"type": "plot", "value": "temp_chart.png"}
```

2023-10-19 14:58:33 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    df = dfs[0]
    grouped_df = df.groupby(['distributor', 'genre']).size().unstack()
    plt.figure(figsize=(12, 6))
    grouped_df.plot(kind='bar')
    plt.xlabel('Distributor')
    plt.ylabel('Count')
    plt.title('Number of Movies by Distributor and Genre')
    plt.legend(title='Genre')
    plt.savefig('temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'temp_chart.png'}
        ```
2023-10-19 15:00:24 [INFO] Question: show a graph between genre and distributor
2023-10-19 15:00:24 [INFO] Running PandasAI with openai LLM...
2023-10-19 15:00:24 [INFO] Prompt ID: 8aa52bdc-2bb2-4884-8f78-79e13d47fea9
2023-10-19 15:00:24 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
4,12/25/2013,47 Ronin,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
5,6/22/2018,Jurassic World: Fallen Kingdom,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
3,5/12/2017,King Arthur: Legend of the Sword,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
2,7/28/1995,Waterworld,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: show a graph between genre and distributor
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-19 15:01:10 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd
import matplotlib.pyplot as plt

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Prepare
    df = dfs[0]
    
    # Process
    genre_distributor = df.groupby(['genre', 'distributor']).size().unstack()
    
    # Analyze
    plt.figure(figsize=(12, 6))
    genre_distributor.plot(kind='bar')
    plt.xlabel('Genre')
    plt.ylabel('Count')
    plt.title('Genre vs Distributor')
    plt.savefig('temp_chart.png')
    plt.close()
    
    return {"type": "plot", "value": "temp_chart.png"}
```

2023-10-19 15:01:10 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    df = dfs[0]
    genre_distributor = df.groupby(['genre', 'distributor']).size().unstack()
    plt.figure(figsize=(12, 6))
    genre_distributor.plot(kind='bar')
    plt.xlabel('Genre')
    plt.ylabel('Count')
    plt.title('Genre vs Distributor')
    plt.savefig('temp_chart.png')
    plt.close()
    return {'type': 'plot', 'value': 'temp_chart.png'}
        ```
2023-10-19 15:05:20 [INFO] Question: show results only for comedy genre and remove the mpaa rating column and show only year in release date column
2023-10-19 15:05:20 [INFO] Running PandasAI with openai LLM...
2023-10-19 15:05:20 [INFO] Prompt ID: 37a2295d-7066-4730-9f91-e176bfef4312
2023-10-19 15:05:20 [INFO] Using prompt: 
You are provided with the following pandas DataFrames:

<dataframe>
Dataframe dfs[0], with 3401 rows and 9 columns.
This is the metadata of the dataframe dfs[0]:
Unnamed: 0,release_date,movie,production_budget,domestic_gross,worldwide_gross,distributor,mpaa_rating,genre
2,7/28/1995,Waterworld,175000000.0,88246220.0,264246220.0,Universal,PG-13,Action
4,12/25/2013,47 Ronin,175000000.0,38362475.0,151716815.0,Universal,PG-13,Action
1,6/22/2007,Evan Almighty,175000000.0,100289690.0,174131329.0,Universal,PG,Comedy
3,5/12/2017,King Arthur: Legend of the Sword,175000000.0,39175066.0,139950708.0,Warner Bros.,PG-13,Adventure
5,6/22/2018,Jurassic World: Fallen Kingdom,170000000.0,416769345.0,1304866322.0,Universal,PG-13,Action
</dataframe>

<conversation>
User 1: show results only for comedy genre and remove the mpaa rating column and show only year in release date column
</conversation>

This is the initial python code to be updated:
```python
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
```

Using the provided dataframes (`dfs`), update the python code based on the last question in the conversation.

Updated code:

2023-10-19 15:06:05 [INFO] Code generated:
```
# TODO import all the dependencies required
import pandas as pd

def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    # Prepare
    df = dfs[0].copy()
    
    # Process
    df['release_date'] = pd.to_datetime(df['release_date'])
    df['release_date'] = df['release_date'].dt.year
    df = df[df['genre'] == 'Comedy']
    df = df.drop(columns=['mpaa_rating'])
    
    # Analyze
    result = {"type": "dataframe", "value": df}
    return result
```

2023-10-19 15:06:05 [INFO] 
Code running:
```
def analyze_data(dfs: list[pd.DataFrame]) ->dict:
    """
    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 plot a chart save it to an image in temp_chart.png and do not show the chart.)
    At the end, return a dictionary of:
    - type (possible values "string", "number", "dataframe", "plot")
    - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
    Examples: 
        { "type": "string", "value": "The highest salary is $9,000." }
        or
        { "type": "number", "value": 125 }
        or
        { "type": "dataframe", "value": pd.DataFrame({...}) }
        or
        { "type": "plot", "value": "temp_chart.png" }
    """
    df = dfs[0].copy()
    df['release_date'] = pd.to_datetime(df['release_date'])
    df['release_date'] = df['release_date'].dt.year
    df = df[df['genre'] == 'Comedy']
    df = df.drop(columns=['mpaa_rating'])
    result = {'type': 'dataframe', 'value': df}
    return result
        ```
2023-10-19 15:06:05 [INFO] Answer: {'type': 'dataframe', 'value':       Unnamed: 0  release_date                        movie  ...  worldwide_gross             distributor   genre
0              1          2007                Evan Almighty  ...      174131329.0               Universal  Comedy
85            86          2005       Fun With Dick And Jane  ...      203018919.0           Sony Pictures  Comedy
189          190          2013               The Hangover 3  ...      362000072.0            Warner Bros.  Comedy
200          201          2010               Little Fockers  ...      310650574.0               Universal  Comedy
204          205          2010               The Other Guys  ...      170936470.0           Sony Pictures  Comedy
...          ...           ...                          ...  ...              ...                     ...     ...
3384        3385          2003              Pieces of April  ...        3571253.0                     MGM  Comedy
3385        3386          1996          When The Cat's Away  ...        2525984.0  Sony Pictures Classics  Comedy
3387        3388          1998         Let's Talk About Sex  ...         373615.0               Fine Line  Comedy
3398        3399          1997  Love and Other Catastrophes  ...         743216.0         Fox Searchlight  Comedy
3399        3400          1998   I Married a Strange Person  ...         203134.0               Lionsgate  Comedy

[813 rows x 8 columns]}
2023-10-19 15:06:05 [INFO] Executed in: 44.568594217300415s