I was soaring with this question for a very long time, as a result I made such a function, it calculates linear regression as on the TradingView function ta.linreg() in PineScript:

import numpy as np

def np_shift(array: np.ndarray, offset: int = 1, fill_value=np.nan):
    result = np.empty_like(array)
    if offset > 0:
        result[:offset] = fill_value
        result[offset:] = array[:-offset]
    elif offset < 0:
        result[offset:] = fill_value
        result[:offset] = array[-offset:]
    else:
        result[:] = array
    return result

def Linreg(source: np.ndarray, length: int, offset: int = 0):
    size = len(source)
    linear = np.zeros(size)

    for i in range(length, size):

        sumX = 0.0
        sumY = 0.0
        sumXSqr = 0.0
        sumXY = 0.0

        for z in range(length):
            val = source[i-z]
            per = z + 1.0
            sumX += per
            sumY += val
            sumXSqr += per * per
            sumXY += val * per

        slope = (length * sumXY - sumX * sumY) / (length * sumXSqr - sumX * sumX)
        average = sumY / length
        intercept = average - slope * sumX / length + slope

        linear[i] = intercept

    if offset != 0:
        linear = np_shift(linear, offset)

    return linear

When you pass the dataframe to pandas_ta it will find the close column so you do not need to specify it. "Close". (KeyError: 'Close') is caused by the column label. You will have to rename the column to 'close' from 'Close'.

import pandas_ta as ta
df[SMA 10] = df.ta.sma(10)
df[SMA 50] = df.ta.sma(50)
df[SMA 100] = df.ta.sma(100)

Alternatively, you could do it this way as well.

sma10 = df.ta.sma(10)
sma50 = df.ta.sma(50)
sma100 = df.ta.sma(100)
df = pd.concat([df, sma10, sma50, sma100], axis=1)