Python pandas.DataFrame方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def average_true_range(df, n):
    """
    
    :param df: pandas.DataFrame
    :param n: 
    :return: pandas.DataFrame
    """
    i = 0
    TR_l = [0]
    while i < df.index[-1]:
        TR = max(df.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
        TR_l.append(TR)
        i = i + 1
    TR_s = pd.Series(TR_l)
    ATR = pd.Series(TR_s.ewm(span=n, min_periods=n).mean(), name='ATR_' + str(n))
    df = df.join(ATR)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def create_lines(self, x, varieties):
        """
        Draw just the data portion.
        """
        lines = pd.DataFrame()
        for i, var in enumerate(varieties):
            self.legend.append(var)
            data = varieties[var]["data"]
            color = get_color(varieties[var], i)
            x_array = np.array(x)
            y_array = np.array(data)
            line = pd.DataFrame({"x": x_array,
                                 "y": y_array,
                                 "color": color,
                                 "var": var})
            lines = lines.append(line, ignore_index=True, sort=False)
        return lines 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def create_scats(self, varieties):
        self.scats = pd.DataFrame(columns=["x", "y", "color", "marker", "var"])
        for i, var in enumerate(varieties):
            self.legend.append(var)
            (x_array, y_array) = self.get_arrays(varieties, var)
            if len(x_array) <= 0:  # no data to graph!
                '''
                I am creating a single "position" for an agent that cannot
                be seen. This seems to fix the issue of colors being
                missmatched in the occasion that a group has no agents.
                '''
                x_array = [-1]
                y_array = [-1]
            elif len(x_array) != len(y_array):
                logging.debug("Array length mismatch in scatter plot")
                return
            color = get_color(varieties[var], i)
            marker = get_marker(varieties[var], i)
            scat = pd.DataFrame({"x": pd.Series(x_array),
                                 "y": pd.Series(y_array),
                                 "color": color,
                                 "marker": marker,
                                 "var": var})
            self.scats = self.scats.append(scat, ignore_index=True,
                                           sort=False) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def run(which):
    print("running all tests...")
    summary = pandas.DataFrame(columns=["pass", "info", "timing"])

    # Test chromosome ends
    if len(which)==0 or "chrom_ends" in which:
        summary.loc["chrom_ends"] = _runTest(runTestIssues, "issues")

    # Run the demos
    if len(which)==0 or "demos" in which:
        summary.loc["demos"] = _runTest(testDemos.run, "demos")

    # Run regression testing on ref/alt/amb counts
    if len(which)==0 or "counts" in which:
        summary.loc["counts"] = _runTest(runTestCounts, "counts")

    # Run the render regression tests
    if len(which)==0 or "rendering" in which:
        summary.loc["rendering"] = _runTest(rendertest.run, "rendering")    

    summary["timing"] = summary["timing"].apply(lambda x: "{}".format(datetime.timedelta(seconds=int(x))))
    print(summary)

    saveTimingInfo(summary) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def write_daily_alert(cls, symbol, stock_id, action):
        """
        write daily stock alert to MongoDB.
        :param symbol: Arctic symbol
        :param data: dict, like: {'stock': '000651', 'action': 'buy/sell'}
        :return: None
        """

        lib = get_or_create_library(conf.DAILY_STOCK_ALERT_LIBNAME)

        data = {
            'stock': stock_id,
            'action': action
        }
        df = pd.DataFrame([data], columns=data.keys())
        if symbol in lib.list_symbols():
            lib.append(symbol, df)
        else:
            lib.write(symbol, df) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def _test_strip_unused_cols(self):
        data = pd.DataFrame({
            'name': ['tom', 'jack'],
            'age': [24, 56],
            'gender': ['male', 'male'],
            'address': ['cn', 'us']
        })
        data.index = pd.date_range(start='2017-01-01', periods=2)

        origin_cols = ['name', 'age', 'gender', 'address']
        unused_cols = ['address', 'gender']
        new_cols = ['name', 'age']

        self.assertEqual(list(data.columns).sort(), origin_cols.sort())

        bdu.Utils.strip_unused_cols(data, *unused_cols)

        self.assertEqual(list(data.columns).sort(), new_cols.sort()) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def ppsr(df):
    """Calculate Pivot Points, Supports and Resistances for given data
    
    :param df: pandas.DataFrame
    :return: pandas.DataFrame
    """
    PP = pd.Series((df['High'] + df['Low'] + df['Close']) / 3)
    R1 = pd.Series(2 * PP - df['Low'])
    S1 = pd.Series(2 * PP - df['High'])
    R2 = pd.Series(PP + df['High'] - df['Low'])
    S2 = pd.Series(PP - df['High'] + df['Low'])
    R3 = pd.Series(df['High'] + 2 * (PP - df['Low']))
    S3 = pd.Series(df['Low'] - 2 * (df['High'] - PP))
    psr = {'PP': PP, 'R1': R1, 'S1': S1, 'R2': R2, 'S2': S2, 'R3': R3, 'S3': S3}
    PSR = pd.DataFrame(psr)
    df = df.join(PSR)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def trix(df, n):
    """Calculate TRIX for given data.
    
    :param df: pandas.DataFrame
    :param n: 
    :return: pandas.DataFrame
    """
    EX1 = df['Close'].ewm(span=n, min_periods=n).mean()
    EX2 = EX1.ewm(span=n, min_periods=n).mean()
    EX3 = EX2.ewm(span=n, min_periods=n).mean()
    i = 0
    ROC_l = [np.nan]
    while i + 1 <= df.index[-1]:
        ROC = (EX3[i + 1] - EX3[i]) / EX3[i]
        ROC_l.append(ROC)
        i = i + 1
    Trix = pd.Series(ROC_l, name='Trix_' + str(n))
    df = df.join(Trix)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def vortex_indicator(df, n):
    """Calculate the Vortex Indicator for given data.
    
    Vortex Indicator described here:
        http://www.vortexindicator.com/VFX_VORTEX.PDF
    :param df: pandas.DataFrame
    :param n: 
    :return: pandas.DataFrame
    """
    i = 0
    TR = [0]
    while i < df.index[-1]:
        Range = max(df.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
        TR.append(Range)
        i = i + 1
    i = 0
    VM = [0]
    while i < df.index[-1]:
        Range = abs(df.loc[i + 1, 'High'] - df.loc[i, 'Low']) - abs(df.loc[i + 1, 'Low'] - df.loc[i, 'High'])
        VM.append(Range)
        i = i + 1
    VI = pd.Series(pd.Series(VM).rolling(n).sum() / pd.Series(TR).rolling(n).sum(), name='Vortex_' + str(n))
    df = df.join(VI)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def true_strength_index(df, r, s):
    """Calculate True Strength Index (TSI) for given data.
    
    :param df: pandas.DataFrame
    :param r: 
    :param s: 
    :return: pandas.DataFrame
    """
    M = pd.Series(df['Close'].diff(1))
    aM = abs(M)
    EMA1 = pd.Series(M.ewm(span=r, min_periods=r).mean())
    aEMA1 = pd.Series(aM.ewm(span=r, min_periods=r).mean())
    EMA2 = pd.Series(EMA1.ewm(span=s, min_periods=s).mean())
    aEMA2 = pd.Series(aEMA1.ewm(span=s, min_periods=s).mean())
    TSI = pd.Series(EMA2 / aEMA2, name='TSI_' + str(r) + '_' + str(s))
    df = df.join(TSI)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def money_flow_index(df, n):
    """Calculate Money Flow Index and Ratio for given data.
    
    :param df: pandas.DataFrame
    :param n: 
    :return: pandas.DataFrame
    """
    PP = (df['High'] + df['Low'] + df['Close']) / 3
    i = 0
    PosMF = [0]
    while i < df.index[-1]:
        if PP[i + 1] > PP[i]:
            PosMF.append(PP[i + 1] * df.loc[i + 1, 'Volume'])
        else:
            PosMF.append(0)
        i = i + 1
    PosMF = pd.Series(PosMF)
    TotMF = PP * df['Volume']
    MFR = pd.Series(PosMF / TotMF)
    MFI = pd.Series(MFR.rolling(n, min_periods=n).mean(), name='MFI_' + str(n))
    df = df.join(MFI)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def on_balance_volume(df, n):
    """Calculate On-Balance Volume for given data.
    
    :param df: pandas.DataFrame
    :param n: 
    :return: pandas.DataFrame
    """
    i = 0
    OBV = [0]
    while i < df.index[-1]:
        if df.loc[i + 1, 'Close'] - df.loc[i, 'Close'] > 0:
            OBV.append(df.loc[i + 1, 'Volume'])
        if df.loc[i + 1, 'Close'] - df.loc[i, 'Close'] == 0:
            OBV.append(0)
        if df.loc[i + 1, 'Close'] - df.loc[i, 'Close'] < 0:
            OBV.append(-df.loc[i + 1, 'Volume'])
        i = i + 1
    OBV = pd.Series(OBV)
    OBV_ma = pd.Series(OBV.rolling(n, min_periods=n).mean(), name='OBV_' + str(n))
    df = df.join(OBV_ma)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def coppock_curve(df, n):
    """Calculate Coppock Curve for given data.
    
    :param df: pandas.DataFrame
    :param n: 
    :return: pandas.DataFrame
    """
    M = df['Close'].diff(int(n * 11 / 10) - 1)
    N = df['Close'].shift(int(n * 11 / 10) - 1)
    ROC1 = M / N
    M = df['Close'].diff(int(n * 14 / 10) - 1)
    N = df['Close'].shift(int(n * 14 / 10) - 1)
    ROC2 = M / N
    Copp = pd.Series((ROC1 + ROC2).ewm(span=n, min_periods=n).mean(), name='Copp_' + str(n))
    df = df.join(Copp)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def keltner_channel(df, n):
    """Calculate Keltner Channel for given data.
    
    :param df: pandas.DataFrame
    :param n: 
    :return: pandas.DataFrame
    """
    KelChM = pd.Series(((df['High'] + df['Low'] + df['Close']) / 3).rolling(n, min_periods=n).mean(),
                       name='KelChM_' + str(n))
    KelChU = pd.Series(((4 * df['High'] - 2 * df['Low'] + df['Close']) / 3).rolling(n, min_periods=n).mean(),
                       name='KelChU_' + str(n))
    KelChD = pd.Series(((-2 * df['High'] + 4 * df['Low'] + df['Close']) / 3).rolling(n, min_periods=n).mean(),
                       name='KelChD_' + str(n))
    df = df.join(KelChM)
    df = df.join(KelChU)
    df = df.join(KelChD)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import DataFrame [as 別名]
def ultimate_oscillator(df):
    """Calculate Ultimate Oscillator for given data.
    
    :param df: pandas.DataFrame
    :return: pandas.DataFrame
    """
    i = 0
    TR_l = [0]
    BP_l = [0]
    while i < df.index[-1]:
        TR = max(df.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
        TR_l.append(TR)
        BP = df.loc[i + 1, 'Close'] - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
        BP_l.append(BP)
        i = i + 1
    UltO = pd.Series((4 * pd.Series(BP_l).rolling(7).sum() / pd.Series(TR_l).rolling(7).sum()) + (
                2 * pd.Series(BP_l).rolling(14).sum() / pd.Series(TR_l).rolling(14).sum()) + (
                                 pd.Series(BP_l).rolling(28).sum() / pd.Series(TR_l).rolling(28).sum()),
                     name='Ultimate_Osc')
    df = df.join(UltO)
    return df