(1) 👨:

create an aggregation table called "total_expenses_by_type.csv" containing the totals for each transaction name, make sure to normalize the transaction name values before generating the table.

🤖:

```python?code_reference&code_event_index=2

import pandas as pd

​

pd.set_option('display.max_rows', None)

pd.set_option('display.max_columns', None)

​

# Read the CSV file into a DataFrame

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

​

# Display the first 5 rows

print(df.head())

​

# Print the column names and their data types

print(df.info())

```

```text?code_stdout&code_event_index=2

      Transaction Name    Date Unit  Income  Expenses         Total

0  Carlos PAYMENT JOSE  25-Dec  NaN     NaN    2000.0    (2,963.00)

1              garbage  25-Dec  NaN     NaN     100.0    (3,063.00)

2      MAINTENENCE JAN     NaN  gh1   990.0       NaN    (2,073.00)

3      MAINTENENCE JAN     NaN  101  1740.0       NaN      (333.00)

4      MAINTENENCE JAN  12-Dec  102  1800.0       NaN     1,467.00 

<class 'pandas.core.frame.DataFrame'>

RangeIndex: 167 entries, 0 to 166

Data columns (total 6 columns):

 #   Column            Non-Null Count  Dtype  

---  ------            --------------  -----  

 0   Transaction Name  160 non-null    object 

 1   Date              109 non-null    object 

 2   Unit              76 non-null     object 

 3   Income            63 non-null     float64

 4   Expenses          91 non-null     float64

 5   Total             167 non-null    object 

dtypes: float64(2), object(4)

memory usage: 8.0+ KB

None

​

```

To calculate the total expenses by transaction type, we'll calculate the sum of the `Expenses` column for each unique value in the `Transaction Name` column. Before doing this, we'll:

​

1. Normalize the transaction names by converting them to lowercase.

2. Fill null values with 0 to avoid errors in the calculation.

​

```python?code_reference&code_event_index=6

​

​

from pandas.api.types import is_numeric_dtype

​

for column_name in ['Total']:

  if not is_numeric_dtype(df[column_name]):

    # Assume CSV columns can only be numeric or string.

    df[column_name] = pd.to_numeric(

        df[column_name].str.replace(',', repl='', regex=True),

        errors='coerce'

    ).fillna(0)

​

df['Transaction Name'] = df['Transaction Name'].str.lower()

​

df_out = (

    df.groupby('Transaction Name')['Expenses'].sum().sort_values(ascending=False)

)

df_out.to_csv('total_expenses_by_type.csv')

​

```

​

[file-tag: total_expenses_by_type.csv]

​

The total expenses by transaction type are available in the file "total_expenses_by_type.csv".

​

-----

​

(2) 👨:

create a new table called "type_percentage_distribution.csv" containing the percentage distribution for the values in the "expense_distributions_by_type.csv" table. Join this column to the "total_expenses_by_type.csv" table on the Transaction Name column

Response A

​

Response B

​

<Code 1>

It seems that the "expense_distributions_by_type.csv" file does not exist. Before proceeding with creating the "type_percentage_distribution.csv" table, we need to generate the "expense_distributions_by_type.csv" file.

​

<Code 2>

​

expense_distributions_by_type.csv

​

Now that the "expense_distributions_by_type.csv" file exists, we can proceed with creating the "type_percentage_distribution.csv" table.

​

<Code 3>

​

type_percentage_distribution.csv

​

The "type_percentage_distribution.csv" table, which includes the percentage distribution for transaction types and their respective expenses, has been created.

​

Below is a preview of the data:

​

Transaction Name    Expenses    Frequency   Percentage

carlos  70350   37  23.125

garbage 4100    37  23.125

first two-month 2022    4019    1   0.625

third two-month water   3938    1   0.625

second two-month 2022   3914    1   0.625

The complete data can be found in the "type_percentage_distribution.csv" file.

​

Response A Code/Output:

​

​

import pandas as pd

​

pd.set_option('display.max_rows', None)

pd.set_option('display.max_columns', None)

​

# Read the CSV files into DataFrames

total_expenses_df = pd.read_csv('total_expenses_by_type.csv')

expense_distributions_df = pd.read_csv('expense_distributions_by_type.csv')

​

# Display the first 5 rows of each DataFrame

print("total_expenses_df.head()\n")

print(total_expenses_df.head().to_markdown(index=False, numalign="left", stralign="left"))

​

print("\nexpense_distributions_df.head()\n")

print(expense_distributions_df.head().to_markdown(index=False, numalign="left", stralign="left"))

​

# Print the column names and their data types for each DataFrame

print("\ntotal_expenses_df.info()\n")

print(total_expenses_df.info())

​

print("\nexpense_distributions_df.info()\n")

print(expense_distributions_df.info())

​

Traceback (most recent call last):

  File "<string>", line 10, in <module>

  File "readers.py", line 912, in read_csv

    return _read(filepath_or_buffer, kwds)

           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "readers.py", line 577, in _read

    parser = TextFileReader(filepath_or_buffer, **kwds)

             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "readers.py", line 1407, in __init__

    self._engine = self._make_engine(f, self.engine)

                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "readers.py", line 1661, in _make_engine

    self.handles = get_handle(

                   ^^^^^^^^^^^

  File "common.py", line 865, in get_handle

    handle = open(

             ^^^^^

FileNotFoundError: [Errno 2] No such file or directory: 'expense_distributions_by_type.csv'

​

​

# Group by `Transaction Name` and count occurrences

expense_distributions_df = (

    df["Transaction Name"]

    .value_counts()

    .reset_index(name="Frequency")

    .rename(columns={"index": "Transaction Name"})

)

​

# Save the results to a CSV file

expense_distributions_df.to_csv("expense_distributions_by_type.csv", index=False)

​

# Display the first 5 rows

print(expense_distributions_df.head().to_markdown(index=False, numalign="left", stralign="left"))

​

# Print the column names and their data types

print(expense_distributions_df.info())

​

| Transaction Name   | Frequency   |

|:-------------------|:------------|

| carlos             | 37          |

| garbage            | 37          |

| maintenence jun    | 8           |

| maintenence aug    | 8           |

| maintenence sep    | 8           |

<class 'pandas.core.frame.DataFrame'>

RangeIndex: 25 entries, 0 to 24

Data columns (total 2 columns):

 #   Column            Non-Null Count  Dtype 

---  ------            --------------  ----- 

 0   Transaction Name  25 non-null     object

 1   Frequency         25 non-null     int64 

dtypes: int64(1), object(1)

memory usage: 532.0+ bytes

None

​

​

# Calculate the total sum of frequencies

total_frequency = expense_distributions_df["Frequency"].sum()

​

# Calculate percentage distribution

expense_distributions_df["Percentage"] = (

    expense_distributions_df["Frequency"] / total_frequency * 100

)

​

# Merge the two DataFrames on 'Transaction Name'

merged_df = pd.merge(

    total_expenses_df, expense_distributions_df, on="Transaction Name", how="left"

)

​

# Save the merged DataFrame to a CSV file

merged_df.to_csv("type_percentage_distribution.csv", index=False)

​

# Display the first 5 rows

print(merged_df.head().to_markdown(index=False, numalign="left", stralign="left"))

​

# Print the column names and their data types

print(merged_df.info())

​

| Transaction Name      | Expenses   | Frequency   | Percentage   |

|:----------------------|:-----------|:------------|:-------------|

| carlos                | 70350      | 37          | 23.125       |

| garbage               | 4100       | 37          | 23.125       |

| first two-month 2022  | 4019       | 1           | 0.625        |

| third two-month water | 3938       | 1           | 0.625        |

| second two-month 2022 | 3914       | 1           | 0.625        |

<class 'pandas.core.frame.DataFrame'>

RangeIndex: 25 entries, 0 to 24

Data columns (total 4 columns):

 #   Column            Non-Null Count  Dtype  

---  ------            --------------  -----  

 0   Transaction Name  25 non-null     object 

 1   Expenses          25 non-null     float64

 2   Frequency         25 non-null     int64  

 3   Percentage        25 non-null     float64

dtypes: float64(2), int64(1), object(1)

memory usage: 932.0+ bytes

None

​

Response A step-by-step (Use this if Response A was "Partially Correct" or "Incorrect")

​

Step 1: 

event_msg: "create a new table called \"type_percentage_distribution.csv\" containing the percentage distribution for the values in the \"expense_distributions_by_type.csv\" table. Join this column to the \"total_expenses_by_type.csv\" table on the Transaction Name column"

event_tag: EVENT_TAG_USER_QUERY

​

Step 2: 

event_msg: "1. Read \"total_expenses_by_type.csv\" and \"expense_distributions_by_type.csv\" into Pandas Dataframes.\n2. Display the first 5 rows of each DataFrame using the `df.head()` method.\n3. Get information about the columns in each of the DataFrames, including their data types, using the `df.info()` method."

event_tag: EVENT_TAG_CODER_INSTRUCTION

planning_step_index: 1

​

Step 3: 

event_msg: "\n\nimport pandas as pd\n\npd.set_option(\'display.max_rows\', None)\npd.set_option(\'display.max_columns\', None)\n\n# Read the CSV files into DataFrames\ntotal_expenses_df = pd.read_csv(\'total_expenses_by_type.csv\')\nexpense_distributions_df = pd.read_csv(\'expense_distributions_by_type.csv\')\n\n# Display the first 5 rows of each DataFrame\nprint(\"total_expenses_df.head()\\n\")\nprint(total_expenses_df.head().to_markdown(index=False, numalign=\"left\", stralign=\"left\"))\n\nprint(\"\\nexpense_distributions_df.head()\\n\")\nprint(expense_distributions_df.head().to_markdown(index=False, numalign=\"left\", stralign=\"left\"))\n\n# Print the column names and their data types for each DataFrame\nprint(\"\\ntotal_expenses_df.info()\\n\")\nprint(total_expenses_df.info())\n\nprint(\"\\nexpense_distributions_df.info()\\n\")\nprint(expense_distributions_df.info())\n"

event_tag: EVENT_TAG_CODE

remaining_self_refinement_steps: 0

​

Step 4: 

event_msg: "Traceback (most recent call last):\n  File \"<string>\", line 10, in <module>\n  File \"readers.py\", line 912, in read_csv\n    return _read(filepath_or_buffer, kwds)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"readers.py\", line 577, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"readers.py\", line 1407, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"readers.py\", line 1661, in _make_engine\n    self.handles = get_handle(\n                   ^^^^^^^^^^^\n  File \"common.py\", line 865, in get_handle\n    handle = open(\n             ^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: \'expense_distributions_by_type.csv\'\n"

event_tag: EVENT_TAG_CODE_ERROR_OUT

exit_status: FILE_NOT_FOUND

remaining_self_refinement_steps: 0

​

Step 5: 

event_msg: "It seems that the \"expense_distributions_by_type.csv\" file does not exist. Before proceeding with creating the \"type_percentage_distribution.csv\" table, we need to generate the \"expense_distributions_by_type.csv\" file.\n"

event_tag: EVENT_TAG_OUTPUT_TO_USER

planning_step_index: 2

​

Step 6: 

event_msg: "Transform and Aggregate:\n\n1. Group the data by `Transaction Name` and count the occurrences of each name, storing the results in a new dataframe `expense_distributions_df`.\n\nOutput file:\n\n1. Write the `expense_distributions_df` dataframe to a CSV file named \"expense_distributions_by_type.csv\".\n\nDisplay outputs:\n\n1. Print the first 5 rows of `expense_distributions_df` using the `df.head()` method.\n2. Print the column names and their data types of `expense_distributions_df` using the `df.info()` method.\n\n\n\n \n\n\n\n"

event_tag: EVENT_TAG_CODER_INSTRUCTION

planning_step_index: 2

​

Step 7: 

event_msg: "\n\n# Group by `Transaction Name` and count occurrences\nexpense_distributions_df = (\n    df[\"Transaction Name\"]\n    .value_counts()\n    .reset_index(name=\"Frequency\")\n    .rename(columns={\"index\": \"Transaction Name\"})\n)\n\n# Save the results to a CSV file\nexpense_distributions_df.to_csv(\"expense_distributions_by_type.csv\", index=False)\n\n# Display the first 5 rows\nprint(expense_distributions_df.head().to_markdown(index=False, numalign=\"left\", stralign=\"left\"))\n\n# Print the column names and their data types\nprint(expense_distributions_df.info())\n"

event_tag: EVENT_TAG_CODE

remaining_self_refinement_steps: 0

​

Step 8: 

event_msg: ""

event_tag: EVENT_TAG_CODE_MSG_OUT

​

Step 9: 

event_msg: "[expense_distributions_by_type.csv](https://contribution.usercontent.google.com/download?c=CgxiYXJkX3N0b3JhZ2USPxIJZXZhbF9kYXRhGjIKMDAwMDYxNTcyOGVkOGMwZDcwMTMwNzAzN2I3MGQwMzlmYWMyYmM1NTgyNWQ2ZTE0Ng&filename=expense_distributions_by_type.csv&opi=99803183)"

event_tag: EVENT_TAG_CODE_GENERATED_TEXT_FILE_OUT

code_generated_text_files {

  file_tag: "[file-tag: expense_distributions_by_type.csv]"

  file_attachment {

    file_type: FILE_TYPE_TEXT

    name: "expense_distributions_by_type.csv"

    serving_url: "https://contribution.usercontent.google.com/download?c=CgxiYXJkX3N0b3JhZ2USPxIJZXZhbF9kYXRhGjIKMDAwMDYxNTcyOGVkOGMwZDcwMTMwNzAzN2I3MGQwMzlmYWMyYmM1NTgyNWQ2ZTE0Ng&filename=expense_distributions_by_type.csv&opi=99803183"

    file_source: FILE_SOURCE_ICE_GENERATED

    created_time {

      seconds: 1712431499

    }

  }

  file_name: "expense_distributions_by_type.csv"

}

​

Step 10: 

event_msg: "Now that the \"expense_distributions_by_type.csv\" file exists, we can proceed with creating the \"type_percentage_distribution.csv\" table.\n"

event_tag: EVENT_TAG_OUTPUT_TO_USER

planning_step_index: 3

​

Step 11: 

event_msg: "Transform and aggregate metrics:\n\n1. Calculate the total sum of the `Frequency` column in the `expense_distributions_df` DataFrame.\n2. Create a new column called `Percentage` in the `expense_distributions_df` DataFrame, which is calculated by dividing the `Frequency` column by the total sum calculated in the previous step and multiplying by 100.\n\nJoin and output:\n\n1. Merge `total_expenses_df` and `expense_distributions_df` on the `Transaction Name` column.\n2. Write the merged DataFrame to a CSV file named \"type_percentage_distribution.csv\".\n\nDisplay outputs:\n\n1. Print the first 5 rows of the merged DataFrame using the `df.head()` method.\n2. Print the column names and their data types of the merged DataFrame using the `df.info()` method.\n\n\n\n \n\n\n\n"

event_tag: EVENT_TAG_CODER_INSTRUCTION

planning_step_index: 3

​

Step 12: 

event_msg: "\n\n# Calculate the total sum of frequencies\ntotal_frequency = expense_distributions_df[\"Frequency\"].sum()\n\n# Calculate percentage distribution\nexpense_distributions_df[\"Percentage\"] = (\n    expense_distributions_df[\"Frequency\"] / total_frequency * 100\n)\n\n# Merge the two DataFrames on \'Transaction Name\'\nmerged_df = pd.merge(\n    total_expenses_df, expense_distributions_df, on=\"Transaction Name\", how=\"left\"\n)\n\n# Save the merged DataFrame to a CSV file\nmerged_df.to_csv(\"type_percentage_distribution.csv\", index=False)\n\n# Display the first 5 rows\nprint(merged_df.head().to_markdown(index=False, numalign=\"left\", stralign=\"left\"))\n\n# Print the column names and their data types\nprint(merged_df.info())\n"

event_tag: EVENT_TAG_CODE

remaining_self_refinement_steps: 0

​

Step 13: 

event_msg: ""

event_tag: EVENT_TAG_CODE_MSG_OUT

​

Step 14: 

event_msg: "[type_percentage_distribution.csv](https://contribution.usercontent.google.com/download?c=CgxiYXJkX3N0b3JhZ2USPxIJZXZhbF9kYXRhGjIKMDAwMDYxNTcyOGVlOWQyYmQwMTMwNzAzN2I3MGQwMzlmOTVhZTAzOGM0MjU1YmU5Yg&filename=type_percentage_distribution.csv&opi=99803183)"

event_tag: EVENT_TAG_CODE_GENERATED_TEXT_FILE_OUT

code_generated_text_files {

  file_tag: "[file-tag: type_percentage_distribution.csv]"

  file_attachment {

    file_type: FILE_TYPE_TEXT

    name: "type_percentage_distribution.csv"

    serving_url: "https://contribution.usercontent.google.com/download?c=CgxiYXJkX3N0b3JhZ2USPxIJZXZhbF9kYXRhGjIKMDAwMDYxNTcyOGVlOWQyYmQwMTMwNzAzN2I3MGQwMzlmOTVhZTAzOGM0MjU1YmU5Yg&filename=type_percentage_distribution.csv&opi=99803183"

    file_source: FILE_SOURCE_ICE_GENERATED

    created_time {

      seconds: 1712431499

    }

  }

  file_name: "type_percentage_distribution.csv"

}

​

Step 15: 

event_msg: "The \"type_percentage_distribution.csv\" table, which includes the percentage distribution for transaction types and their respective expenses, has been created.\n\nBelow is a preview of the data:\n\n| Transaction Name      |   Expenses |   Frequency |   Percentage |\n|----------------------:|:-----------|:------------|:-------------|\n| carlos                |      70350 |          37 |       23.125 |\n| garbage               |       4100 |          37 |       23.125 |\n| first two-month 2022  |       4019 |           1 |        0.625 |\n| third two-month water |       3938 |           1 |        0.625 |\n| second two-month 2022 |       3914 |           1 |        0.625 |\n\n\n\nThe complete data can be found in the \"type_percentage_distribution.csv\" file."

event_tag: EVENT_TAG_OUTPUT_TO_USER

planning_step_index: 4

​

It seems there was a misunderstanding; the file "expense_distributions_by_type.csv" was not uploaded. Could you please upload this file to proceed with creating the "type_percentage_distribution.csv" table and joining it with the "total_expenses_by_type.csv" table?

​

<Code 1>

​

Response B Code/Output:

​

import pandas as pd

​

# Load the 'expense_distributions_by_type.csv' file provided by the user

distribution_df = pd.read_csv('/mnt/data/expense_distributions_by_type.csv')

​

# Load the 'total_expenses_by_type.csv' file provided by the user

total_expenses_df = pd.read_csv('/mnt/data/total_expenses_by_type.csv')

​

# Display the first few rows of each DataFrame to understand their structure

distribution_df.head(), total_expenses_df.head()

FileNotFoundError Traceback (most recent call last)

Cell In[1], line 4

1 import pandas as pd

3 # Load the 'expense_distributions_by_type.csv' file provided by the user

----> 4 distribution_df = pd.read_csv('/mnt/data/expense_distributions_by_type.csv')

6 # Load the 'total_expenses_by_type.csv' file provided by the user

7 total_expenses_df = pd.read_csv('/mnt/data/total_expenses_by_type.csv')

​

File ~/.local/lib/python3.11/site-packages/pandas/util/_decorators.py:211, in deprecate_kwarg.._deprecate_kwarg..wrapper(*args, **kwargs)

209 else:

210 kwargs[new_arg_name] = new_arg_value

--> 211 return func(*args, **kwargs)

​

File ~/.local/lib/python3.11/site-packages/pandas/util/_decorators.py:331, in deprecate_nonkeyword_arguments..decorate..wrapper(*args, **kwargs)

325 if len(args) > num_allow_args:

326 warnings.warn(

327 msg.format(arguments=_format_argument_list(allow_args)),

328 FutureWarning,

329 stacklevel=find_stack_level(),

330 )

--> 331 return func(*args, **kwargs)

​

File ~/.local/lib/python3.11/site-packages/pandas/io/parsers/readers.py:950, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)

935 kwds_defaults = _refine_defaults_read(

936 dialect,

937 delimiter,

(...)

946 defaults={"delimiter": ","},

947 )

948 kwds.update(kwds_defaults)

--> 950 return _read(filepath_or_buffer, kwds)

​

File ~/.local/lib/python3.11/site-packages/pandas/io/parsers/readers.py:605, in _read(filepath_or_buffer, kwds)

602 _validate_names(kwds.get("names", None))

604 # Create the parser.

--> 605 parser = TextFileReader(filepath_or_buffer, **kwds)

607 if chunksize or iterator:

608 return parser

​

File ~/.local/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1442, in TextFileReader.init(self, f, engine, **kwds)

1439 self.options["has_index_names"] = kwds["has_index_names"]

1441 self.handles: IOHandles | None = None

-> 1442 self._engine = self._make_engine(f, self.engine)

​

File ~/.local/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1735, in TextFileReader._make_engine(self, f, engine)

1733 if "b" not in mode:

1734 mode += "b"

-> 1735 self.handles = get_handle(

1736 f,

1737 mode,

1738 encoding=self.options.get("encoding", None),

1739 compression=self.options.get("compression", None),

1740 memory_map=self.options.get("memory_map", False),

1741 is_text=is_text,

1742 errors=self.options.get("encoding_errors", "strict"),

1743 storage_options=self.options.get("storage_options", None),

1744 )

1745 assert self.handles is not None

1746 f = self.handles.handle

​

File ~/.local/lib/python3.11/site-packages/pandas/io/common.py:856, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)

851 elif isinstance(handle, str):

852 # Check whether the filename is to be opened in binary mode.

853 # Binary mode does not support 'encoding' and 'newline'.

854 if ioargs.encoding and "b" not in ioargs.mode:

855 # Encoding

--> 856 handle = open(

857 handle,

858 ioargs.mode,

859 encoding=ioargs.encoding,

860 errors=errors,

861 newline="",

862 )

863 else:

864 # Binary mode

865 handle = open(handle, ioargs.mode)

​

FileNotFoundError: [Errno 2] No such file or directory: '/mnt/data/expense_distributions_by_type.csv'

​

​