Python pandas.DataFrame() Examples

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 

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 

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) 

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) 

def calculate_scores_of_all(self, saveflag=0, savefilename=''):
        dates = os.listdir(self.article_dir)
        all_date_score=[]
        for date in dates:
            try:
                score,info=self.score_of_date(date)
                all_date_score.append((date,score))
            except:
                continue
        if saveflag:
            rawdata=pd.DataFrame(all_date_score)
            pd.DataFrame.to_csv(rawdata, savefilename)
        return all_date_score,dates 

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) 

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()) 

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 

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 

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 

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 

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 

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 

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 

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 

def donchian_channel(df, n):
    """Calculate donchian channel of given pandas data frame.
    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    i = 0
    dc_l = []
    while i < n - 1:
        dc_l.append(0)
        i += 1

    i = 0
    while i + n - 1 < df.index[-1]:
        dc = max(df['High'].ix[i:i + n - 1]) - min(df['Low'].ix[i:i + n - 1])
        dc_l.append(dc)
        i += 1

    donchian_chan = pd.Series(dc_l, name='Donchian_' + str(n))
    donchian_chan = donchian_chan.shift(n - 1)
    return df.join(donchian_chan) 

def render(self, total_records=None):
        """use BackGrid JS library to render Pandas DataFrame"""
        # if project given, this will result in an overview table of contributions
        # TODO check for index column in df other than the default numbering
        jtable = json.dumps(self.to_backgrid_dict())
        if total_records is None:
            total_records = self.shape[0]
        config = {"total_records": total_records}
        config["uuids"] = [str(uuid.uuid4()) for i in range(4)]
        if self.tid:
            config["tid"] = self.tid
            config["per_page"] = self.per_page
        else:
            config["project"] = self.project
        config["api_key"] = self.api_key
        config["ncols"] = self.ncols
        config["filters"] = self.filters
        jconfig = json.dumps(config)
        html = '<div class="col-md-6" id="{}"></div>'.format(config["uuids"][0])
        html += '<div class="pull-right" id="{}"></div>'.format(config["uuids"][3])
        html += '<div id="{}" style="width:100%;"></div>'.format(config["uuids"][1])
        html += '<div id="{}"></div>'.format(config["uuids"][2])
        html += f"<script>render_table({{table: {jtable}, config: {jconfig}}})</script>"
        return html 

def get_table(results, letter):
    y = "Δ{}".format(letter)
    df = Table(
        RecursiveDict([("δ", results[0]), (y, results[1]), (y + "ₑᵣᵣ", results[2])])
    )
    x0, x1 = map(float, df["δ"].iloc[[0, -1]])
    pad = 0.15 * (x1 - x0)
    mask = (results[3] > x0 - pad) & (results[3] < x1 + pad)
    x, fit = results[3][mask], results[4][mask]
    df.set_index("δ", inplace=True)
    df2 = pd.DataFrame(RecursiveDict([("δ", x), (y + " Fit", fit)]))
    df2.set_index("δ", inplace=True)
    cols = ["δ", y, y + "ₑᵣᵣ", y + " Fit"]
    return (
        pd.concat([df, df2], sort=True)
        .sort_index()
        .reset_index()
        .rename(columns={"index": "δ"})
        .fillna("")[cols]
    ) 

def test_session(integration_url, capsys, session_kind, params):

    assert livy_available(integration_url)

    with LivySession.create(integration_url, kind=session_kind) as session:

        assert session.state == SessionState.IDLE

        session.run(params.print_foo_code)
        assert capsys.readouterr() == (params.print_foo_output, "")

        session.run(params.create_dataframe_code)
        capsys.readouterr()

        session.run(params.dataframe_count_code)
        assert capsys.readouterr() == (params.dataframe_count_output, "")

        with pytest.raises(SparkRuntimeError):
            session.run(params.error_code)

        expected = pandas.DataFrame({"value": range(100)})
        assert session.read("df").equals(expected)

    assert session_stopped(integration_url, session.session_id) 

def prepro_pos_table(pos_tables):
    """Extracts unique positions and sorts them."""
    if not isinstance(pos_tables, list):
        pos_tables = [pos_tables]

    pos_table = None
    for next_pos_table in pos_tables:
        if pos_table is None:
            pos_table = next_pos_table
        else:
            pos_table = pd.concat([pos_table, next_pos_table])
        pos_table = pos_table.groupby('chromo').apply(
            lambda df: pd.DataFrame({'pos': np.unique(df['pos'])}))
        pos_table.reset_index(inplace=True)
        pos_table = pos_table[['chromo', 'pos']]
        pos_table.sort_values(['chromo', 'pos'], inplace=True)
    return pos_table 

def get_df(vcf_file, model_name):
    df = pd.DataFrame(list(KipoiVCFParser(vcf_file)))
    meta_info  = df[["variant_chr", "variant_pos", "variant_ref", "variant_alt", "variant_id"]]
    meta_info["variant_uid"] = df["variant_chr"].astype(str) + ':' + df["variant_pos"].astype(str) + ':' + df["variant_ref"] + ':' + df["variant_alt"]
    df.index = meta_info["variant_uid"]
    meta_info.index = meta_info["variant_uid"]
    obsolete_variant_columns = ["variant_chr", "variant_pos", "variant_ref", "variant_alt", "variant_id"]
    df = df[[col for col in df.columns if col not in obsolete_variant_columns]]
    df = df[[col for col in df.columns if "rID" not in col]]
    col_types = ["_LOGIT_REF", "_LOGIT_ALT", "_REF", "_ALT", "_DIFF", "_LOGIT"]
    if model_name == "labranchor":
        df = average_labranchor(df, model_name, col_types)
    else:
        df.columns = [refmt_col(col, model_name, col_types) for col in df.columns]
    # clump variants together
    df = deduplicate_vars(df)
    # subset meta_info like df and add variant_uid as common ID
    meta_info=meta_info.loc[df.index,:]
    return df, meta_info 

def get_df(vcf_file, model_name):
    df = pd.DataFrame(list(KipoiVCFParser(vcf_file)))
    meta_info  = df[["variant_chr", "variant_pos", "variant_ref", "variant_alt", "variant_id"]]
    meta_info["variant_uid"] = df["variant_chr"].astype(str) + ':' + df["variant_pos"].astype(str) + ':' + df["variant_ref"] + ':' + df["variant_alt"]
    df.index = meta_info["variant_uid"]
    meta_info.index = meta_info["variant_uid"]
    obsolete_variant_columns = ["variant_chr", "variant_pos", "variant_ref", "variant_alt", "variant_id"]
    df = df[[col for col in df.columns if col not in obsolete_variant_columns]]
    df = df[[col for col in df.columns if "rID" not in col]]
    col_types = ["_LOGIT_REF", "_LOGIT_ALT", "_REF", "_ALT", "_DIFF", "_LOGIT"]
    if model_name == "labranchor":
        df = average_labranchor(df, model_name, col_types)
    else:
        df.columns = [refmt_col(col, model_name, col_types) for col in df.columns]
    # clump variants together
    df = deduplicate_vars(df)
    # subset meta_info like df and add variant_uid as common ID
    meta_info=meta_info.loc[df.index,:]
    return df, meta_info 

def display(self):
        try:
            import pandas
            pandas.options.display.width = 250
            pandas.options.display.max_rows = 400
            df = pandas.DataFrame(self.stats, columns=self.header)
            print(df.pivot_table(values="value", index=["variant","sample","allele"], columns="key"))
        except:
            print(str(self)) 

def run():
    commands = ["svviz demo 1 -a --no-web",
                "svviz demo 2 -a --no-web",
                "svviz demo 2 -a --no-web --auto-export",
                "svviz demo 3 -a --no-web"]
    times = []

    for command in commands:
        t0 = time.time()
        app.run(command.split(" "))
        times.append(time.time() - t0)

    print(pandas.DataFrame({"command":commands, "time (s)":times}))

    return (True, "") 

def _ingest_summary(self):
        # get date parameters.
        yr = self._date[:4]
        mn = self._date[4:6]
        dy = self._date[6:]

        self._logger.info("Getting ingest summary data for the day")
        
        ingest_summary_cols = ["date","total"]		
        result_rows = []        
        df_filtered =  pd.DataFrame()

        query_to_load = ("""
            SELECT frame_time, COUNT(*) as total FROM {0}.{1}
            WHERE y={2} AND m={3} AND d={4} AND unix_tstamp IS NOT NULL
            AND frame_time IS NOT NULL AND frame_len IS NOT NULL
            AND dns_qry_name IS NOT NULL AND ip_src IS NOT NULL
            AND (dns_qry_class IS NOT NULL AND dns_qry_type IS NOT NULL
            AND dns_qry_rcode IS NOT NULL ) GROUP BY frame_time;
        """).format(self._db,self._table_name, yr, mn, dy)

        results = impala.execute_query_as_list(query_to_load)
        df = pd.DataFrame(results)

        # Forms a new dataframe splitting the minutes from the time column
        df_new = pd.DataFrame([["{0}-{1}-{2} {3}:{4}".format(yr, mn, dy,\
            val['frame_time'].replace("  "," ").split(" ")[3].split(":")[0].zfill(2),\
            val['frame_time'].replace("  "," ").split(" ")[3].split(":")[1].zfill(2)),\
            int(val['total']) if not math.isnan(val['total']) else 0 ] for key,val in df.iterrows()],columns = ingest_summary_cols)

        #Groups the data by minute
        sf = df_new.groupby(by=['date'])['total'].sum()
        df_per_min = pd.DataFrame({'date':sf.index, 'total':sf.values})

        df_final = df_filtered.append(df_per_min, ignore_index=True).to_records(False,False)

        if len(df_final) > 0:
            query_to_insert=("""
                INSERT INTO {0}.dns_ingest_summary PARTITION (y={1}, m={2}, d={3}) VALUES {4};
            """).format(self._db, yr, mn, dy, tuple(df_final))
            impala.execute_query(query_to_insert) 

def calculate_nlpv(min_count,size,saveflag):
    nlp=NLP()
    nlp.load_model(model_place+str(min_count)+'_'+str(size)+'\\wiki_nlp_'+str(min_count)+'_'+str(size)+'.model')
    words=[]
    fp=open(common_words_filename,'r',encoding='utf-8')
    for line in fp.readlines():
        try:
            temp=nlp.model.wv.__getitem__(line.strip('\n')) # 检查词是否在word2vec模型中
            words.append(line.strip('\n'))
        except:
            continue
    try:
        vector=nlp.nlp_vector(words)
    except:
        vector=nlp.safe_nlp_vector(words)
    
    
    if saveflag:
        vector_transform = vector.T
        names=[]
        for index in nlp.Label_index:
            names.append(index+'w2v')
        for index in nlp.Label_index:
            names.append(index+'wn')
        text = pd.DataFrame(columns = names, data = vector_transform)
        #生成的csv文件的地址
        text.to_csv(save_result_place+'common_words_vector_'+str(min_count)+'_'+str(size)+'.csv') 

def score_of_common_words(self, Min_count, Size, saveflag=1, savefilename=''):
        """
        calculate scores of common words, and save the results as you like.
        
        p.s. please make sure you have set savefilename.
        """
        self.set_model_parameters(Min_count, Size)
        table=pd.read_csv(self.nlp_vector_filename)
        #table=table.abs() # 余弦相似度直接取绝对值
        result=['']*table.shape[0]
        score=[0]*table.shape[0]
        label_num=(table.shape[1]-1)/2
        for i in range(table.shape[0]): 
            w2v=table.iloc[i,1:label_num+1]
            wn=table.iloc[i,len(label_num)+1:len(label_num)*2+1]
            result[i]=self.get_topn_topm(w2v, wn, n=9, m=3) # 这是一个字符串Index
            for reword in result[i]:
                score[i]+=table.loc[i, reword+'w2v']*self.nlp.Label_dict[reword]
            score[i]/=len(result[i])
        
        if saveflag:
            try:
                fp=open(self.valid_word_filename,'r',encoding='utf-8')
                txtlist=fp.readlines()
            except:
                fp=open(self.valid_word_filename,'r',encoding='gbk')
                txtlist=fp.readlines()
            valid_words=[]
            for t in txtlist:
                t=t.split('\n')[0]
                valid_words.append(t)
            fp.close()
            rawdata=pd.DataFrame(score, valid_words)
            pd.DataFrame.to_csv(rawdata, savefilename,encoding='gkb') 

def split_data(cls, data, percent=0.3):
        """
        Split the data into training data and test data.
        :param data(DataFrame): data to be split.
        :param percent(float): percent of data used as training data.
        :return: training data(DataFrame) and testing data(DataFrame)
        """

        rows = len(data)
        train_rows = math.floor(rows * percent)
        test_rows = rows - train_rows

        return data.iloc[:train_rows], data.iloc[-test_rows:] 

def get_best_params(cls, al_results):
        """
        Get the best params, current algorithm is the largest total return rate.
        :param al_results(list): all the optional params and corresponding analysis data.
        :return: best params and corresponding analysis data(dict)
        """
        al_results_df = pd.DataFrame.from_dict(al_results)
        al_results_df = al_results_df.sort_values('total_return_rate', ascending=False)

        al_result_dict = al_results_df.iloc[0].to_dict()

        return al_result_dict 

def save_training_params(symbol, params):
    """
    save training params to library.
    :param symbol: str, arctic symbol
    :param params: dict, e.g.: {"ma_period_s": 1, "ma_period_l": 2, "stock_id": "600909"}
    :return: None
    """

    stock_id = params.ma_periods['stock_id']
    params_to_save = dict(params=params)
    df = pd.DataFrame([params_to_save], columns=params_to_save.keys(), index=[stock_id])

    # write to database
    # if library does not exist, create it
    lib = get_or_create_library(conf.STRATEGY_PARAMS_LIBNAME)

    if lib.has_symbol(symbol):
        logger.debug(
            f'symbol: {symbol} already exists, '
            f'change the params of stock {stock_id}, '
            f'then delete and write symbol: {symbol}.'
        )
        params_df = lib.read(symbol).data
        params_df.loc[stock_id, 'params'] = params
        lib.delete(symbol)
        lib.write(symbol, params_df)
    else:
        logger.debug(
            f'write the params of stock {stock_id} to symbol: {symbol}'
        )
        lib.write(symbol, df)