I can give an alternative code for this indicator from a library I'm developing for learning purposes:

def RSI(data: pd.DataFrame, window_length=14) -> pd.Series:
    """
    Calculate the RSI indicator on a moving window.
    Ref: https://www.investopedia.com/terms/r/rsi.asp
    """
    df_ = data.copy()
    close = df_['Close']
    delta = close.diff()
    up = delta.clip(lower=0)  # Convert negatives to zero.
    down = -1 * delta.clip(upper=0)  # Convert positives to zero.
    # WMA
    roll_up = up.ewm(com=window_length - 1, adjust=True, min_periods=window_length).mean()
    roll_down = down.ewm(com=window_length - 1, adjust=True, min_periods=window_length).mean()
    # Calculate the RSI
    RS = roll_up / roll_down
    return 100.0 - (100.0 / (1.0 + RS))


def Stochastic_RSI(data: pd.DataFrame, window_length=14) -> pd.Series:
    """
    The Stochastic RSI (StochRSI) is an indicator used in technical analysis that ranges between zero and one (or zero and 100 on
    some charting platforms) and is created by applying the Stochastic oscillator formula to a set of relative strength index (RSI)
    values rather than to standard price data.
    Ref: https://www.investopedia.com/terms/s/stochrsi.asp
    """
    df_ = data.copy()
    rsi = RSI(data=df_, window_length=window_length)
    return (rsi - rsi.rolling(window_length).min()) / (rsi.rolling(window_length).max() - rsi.rolling(window_length).min())

A graph:

You can use the following simple function to handle both slow and fast stochastics.

def stochastics(dataframe, low, high, close, k, d ):
    """
    Fast stochastic calculation
    %K = (Current Close - Lowest Low)/
    (Highest High - Lowest Low) * 100
    %D = 3-day SMA of %K

    Slow stochastic calculation
    %K = %D of fast stochastic
    %D = 3-day SMA of %K

    When %K crosses above %D, buy signal 
    When the %K crosses below %D, sell signal
    """

    df = dataframe.copy()

    # Set minimum low and maximum high of the k stoch
    low_min  = df[low].rolling( window = k ).min()
    high_max = df[high].rolling( window = k ).max()

    # Fast Stochastic
    df['k_fast'] = 100 * (df[close] - low_min)/(high_max - low_min)
    df['k_fast'].ffill(inplace=True)
    df['d_fast'] = df['k_fast'].rolling(window = d).mean()

    # Slow Stochastic
    df['k_slow'] = df["d_fast"]
    df['d_slow'] = df['k_slow'].rolling(window = d).mean()

    return df


stochs = stochastics( df, 'Low', 'High', 'Close', 14, 3 )
slow_k = stochs['k_slow'].values
fast_k = stochs['k_fast'].values