Function stocks

plotly/data/__init__.py:225–266

Each row in this wide dataset represents closing prices from 6 tech stocks in 2018/2019. Parameters ---------- indexed: bool Whether or not the 'date' column is used as the index and the column index is named 'company'. Applicable only if `return_type='pandas'`

(indexed=False, datetimes=False, return_type="pandas")

def stocks(indexed=False, datetimes=False, return_type="pandas"):
    """
    Each row in this wide dataset represents closing prices from 6 tech stocks in 2018/2019.

    Parameters
    ----------
    indexed: bool
        Whether or not the 'date' column is used as the index and the column index
        is named 'company'. Applicable only if `return_type='pandas'`

    datetimes: bool
        Whether or not the 'date' column will be of datetime type

    return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'}
        Type of the resulting dataframe

    Returns
    -------
    Dataframe of `return_type` type
        Dataframe with 100 rows and the following columns:
        `['date', 'GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT']`.
        If `indexed` is True, the 'date' column is used as the index and the column index
        is named 'company'
        If `datetimes` is True, the 'date' column will be a datetime column
    """
    if indexed and return_type not in BACKENDS_WITH_INDEX_SUPPORT:
        msg = f"Backend '{return_type}' does not support setting index"
        raise NotImplementedError(msg)

    df = nw.from_native(
        _get_dataset("stocks", return_type=return_type), eager_only=True
    ).with_columns(nw.col("date").cast(nw.String()))

    if datetimes:
        df = df.with_columns(nw.col("date").str.to_datetime())

    if indexed:  # then it must be pandas
        df = df.to_native().set_index("date")
        df.columns.name = "company"
        return df

    return df.to_native()


def experiment(indexed=False, return_type="pandas"):