AutoSkill Custom Python DataFrame Class Implementation

Implement a custom DataFrame class in Python with specific methods (__init__, set_index, __setitem__, __getitem__, loc, iteritems, iterrows, as_type, drop, mean, __repr__) that handles list-based data inputs and outputs CSV-formatted strings with row indices.

install

source · Clone the upstream repo

git clone https://github.com/ECNU-ICALK/AutoSkill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ECNU-ICALK/AutoSkill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/custom-python-dataframe-class-implementation" ~/.claude/skills/ecnu-icalk-autoskill-custom-python-dataframe-class-implementation && rm -rf "$T"

manifest: SkillBank/ConvSkill/english_gpt4_8_GLM4.7/custom-python-dataframe-class-implementation/SKILL.md

source content

Custom Python DataFrame Class Implementation

Implement a custom DataFrame class in Python with specific methods (init, set_index, setitem, getitem, loc, iteritems, iterrows, as_type, drop, mean, repr) that handles list-based data inputs and outputs CSV-formatted strings with row indices.

Prompt

Role & Objective

You are a Python developer tasked with implementing a custom DataFrame class. The class must support specific methods and handle data input/output in a particular format.

Operational Rules & Constraints

Class Structure: Implement a class named
```
DataFrame
```
.
Attributes:
- ```
self.index
```
  : A dictionary to map text to row index.
- ```
self.data
```
  : A dictionary where keys are column names and values are
```
ListV2
```
  objects.
- ```
self.columns
```
  : A list or tuple of column names.
Methods:
- ```
__init__(self, data, columns)
```
  : Initialize the DataFrame. Handle
```
data
```
  as a list of lists (rows) or a dictionary. If
```
data
```
  is a list, populate
```
self.data
```
  by iterating through rows and zipping with
```
columns
```
  .
- ```
set_index(self, index)
```
  : Set the index using a column name.
- ```
__setitem__(self, col_name, values)
```
  : Set or add a column.
- ```
__getitem__(self, col_name)
```
  : Retrieve data. If
```
col_name
```
  is a string, return the list of values for that column. If
```
col_name
```
  is a list of strings, return a CSV-formatted string containing the index and those columns.
- ```
loc(self, row_name)
```
  : Retrieve a row by index name.
- ```
iteritems(self)
```
  : Iterate over columns.
- ```
iterrows(self)
```
  : Iterate over rows.
- ```
as_type(self, dtype, col_name)
```
  : Convert data types.
- ```
drop(self, col_name)
```
  : Remove a column.
- ```
mean(self)
```
  : Calculate mean for columns.
- ```
__repr__(self)
```
  : Return a string representation of the DataFrame.
Output Format:
- ```
__repr__
```
  and multi-column
```
__getitem__
```
  must return a CSV-formatted string.
- The header row must start with an empty string (e.g.,
```
,Col1,Col2
```
  ).
- Subsequent rows must start with the row index (e.g.,
```
0,val1,val2
```
  ).
- Ensure proper handling of tuple/list concatenation when building the table structure.
Helper Class: Use a
```
ListV2
```
class to wrap lists, ensuring it has an
```
append
```
method or behaves like a list for data population.

Anti-Patterns

Do not use pandas or external libraries unless specified.
Do not return the DataFrame object itself when
```
__getitem__
```
receives a list of columns; return the formatted string.
Do not assume
```
self.columns
```
is always a list; handle tuples as well.

Triggers

implement dataframe class
custom dataframe python
dataframe init getitem
fix dataframe error
dataframe csv output