Functions that calculate RSI and StochRSI which give the same value as Trading View. I wrote these functions as RSI and StochRSI functions from TA-Lib give different values as TV.

RSI_and_StochRSI.py

# calculating RSI (gives the same values as TradingView)
# https://stackoverflow.com/questions/20526414/relative-strength-index-in-python-pandas
def RSI(series, period=14):
    delta = series.diff().dropna()
    ups = delta * 0
    downs = ups.copy()
    ups[delta > 0] = delta[delta > 0]
    downs[delta < 0] = -delta[delta < 0]
    ups[ups.index[period-1]] = np.mean( ups[:period] ) #first value is sum of avg gains
    ups = ups.drop(ups.index[:(period-1)])
    downs[downs.index[period-1]] = np.mean( downs[:period] ) #first value is sum of avg losses
    downs = downs.drop(downs.index[:(period-1)])
    rs = ups.ewm(com=period-1,min_periods=0,adjust=False,ignore_na=False).mean() / \
         downs.ewm(com=period-1,min_periods=0,adjust=False,ignore_na=False).mean() 
    return 100 - 100 / (1 + rs)


# calculating Stoch RSI (gives the same values as TradingView)
# https://www.tradingview.com/wiki/Stochastic_RSI_(STOCH_RSI) 
def StochRSI(series, period=14, smoothK=3, smoothD=3):
    # Calculate RSI 
    delta = series.diff().dropna()
    ups = delta * 0
    downs = ups.copy()
    ups[delta > 0] = delta[delta > 0]
    downs[delta < 0] = -delta[delta < 0]
    ups[ups.index[period-1]] = np.mean( ups[:period] ) #first value is sum of avg gains
    ups = ups.drop(ups.index[:(period-1)])
    downs[downs.index[period-1]] = np.mean( downs[:period] ) #first value is sum of avg losses
    downs = downs.drop(downs.index[:(period-1)])
    rs = ups.ewm(com=period-1,min_periods=0,adjust=False,ignore_na=False).mean() / \
         downs.ewm(com=period-1,min_periods=0,adjust=False,ignore_na=False).mean() 
    rsi = 100 - 100 / (1 + rs)

    # Calculate StochRSI 
    stochrsi  = (rsi - rsi.rolling(period).min()) / (rsi.rolling(period).max() - rsi.rolling(period).min())
    stochrsi_K = stochrsi.rolling(smoothK).mean()
    stochrsi_D = stochrsi_K.rolling(smoothD).mean()

    return stochrsi, stochrsi_K, stochrsi_D


# calculating Stoch RSI 
#  -- Same as the above function but uses EMA, not SMA
def StochRSI_EMA(series, period=14, smoothK=3, smoothD=3):
    # Calculate RSI 
    delta = series.diff().dropna()
    ups = delta * 0
    downs = ups.copy()
    ups[delta > 0] = delta[delta > 0]
    downs[delta < 0] = -delta[delta < 0]
    ups[ups.index[period-1]] = np.mean( ups[:period] ) #first value is sum of avg gains
    ups = ups.drop(ups.index[:(period-1)])
    downs[downs.index[period-1]] = np.mean( downs[:period] ) #first value is sum of avg losses
    downs = downs.drop(downs.index[:(period-1)])
    rs = ups.ewm(com=period-1,min_periods=0,adjust=False,ignore_na=False).mean() / \
         downs.ewm(com=period-1,min_periods=0,adjust=False,ignore_na=False).mean() 
    rsi = 100 - 100 / (1 + rs)

    # Calculate StochRSI 
    stochrsi  = (rsi - rsi.rolling(period).min()) / (rsi.rolling(period).max() - rsi.rolling(period).min())
    stochrsi_K = stochrsi.ewm(span=smoothK).mean()
    stochrsi_D = stochrsi_K.ewm(span=smoothD).mean()

    return stochrsi, stochrsi_K, stochrsi_D