Python pandas.read_pickle方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def test_write_explicit(self, compression, get_random_path):
        base = get_random_path
        path1 = base + ".compressed"
        path2 = base + ".raw"

        with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
            df = tm.makeDataFrame()

            # write to compressed file
            df.to_pickle(p1, compression=compression)

            # decompress
            with tm.decompress_file(p1, compression=compression) as f:
                with open(p2, "wb") as fh:
                    fh.write(f.read())

            # read decompressed file
            df2 = pd.read_pickle(p2, compression=None)

            tm.assert_frame_equal(df, df2) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def test_write_infer(self, ext, get_random_path):
        base = get_random_path
        path1 = base + ext
        path2 = base + ".raw"
        compression = None
        for c in self._compression_to_extension:
            if self._compression_to_extension[c] == ext:
                compression = c
                break

        with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
            df = tm.makeDataFrame()

            # write to compressed file by inferred compression method
            df.to_pickle(p1)

            # decompress
            with tm.decompress_file(p1, compression=compression) as f:
                with open(p2, "wb") as fh:
                    fh.write(f.read())

            # read decompressed file
            df2 = pd.read_pickle(p2, compression=None)

            tm.assert_frame_equal(df, df2) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def round_trip_pickle(obj, path=None):
    """
    Pickle an object and then read it again.

    Parameters
    ----------
    obj : pandas object
        The object to pickle and then re-read.
    path : str, default None
        The path where the pickled object is written and then read.

    Returns
    -------
    round_trip_pickled_object : pandas object
        The original object that was pickled and then re-read.
    """

    if path is None:
        path = u('__{random_bytes}__.pickle'.format(random_bytes=rands(10)))
    with ensure_clean(path) as path:
        pd.to_pickle(obj, path)
        return pd.read_pickle(path) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def load_guesses(directory: str, output_type='char', folds=c.GUESSER_GENERATION_FOLDS) -> pd.DataFrame:
        """
        Loads all the guesses pertaining to a guesser inferred from directory
        :param directory: where to load guesses from
        :param output_type: One of: char, full, first
        :param folds: folds to load, by default all of them
        :return: guesses across all folds for given directory
        """
        assert len(folds) > 0
        guess_df = None
        for fold in folds:
            input_path = AbstractGuesser.guess_path(directory, fold, output_type)
            if guess_df is None:
                guess_df = pd.read_pickle(input_path)
            else:
                new_guesses_df = pd.read_pickle(input_path)
                guess_df = pd.concat([guess_df, new_guesses_df])

        return guess_df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def split_dataset(data_path='data-multi-visit.pkl'):
    data = pd.read_pickle(data_path)
    sample_id = data['SUBJECT_ID'].unique()

    random_number = [i for i in range(len(sample_id))]
#     shuffle(random_number)

    train_id = sample_id[random_number[:int(len(sample_id)*2/3)]]
    eval_id = sample_id[random_number[int(
        len(sample_id)*2/3): int(len(sample_id)*5/6)]]
    test_id = sample_id[random_number[int(len(sample_id)*5/6):]]

    def ls2file(list_data, file_name):
        with open(file_name, 'w') as fout:
            for item in list_data:
                fout.write(str(item) + '\n')

    ls2file(train_id, 'train-id.txt')
    ls2file(eval_id, 'eval-id.txt')
    ls2file(test_id, 'test-id.txt')

    print('train size: %d, eval size: %d, test size: %d' %
          (len(train_id), len(eval_id), len(test_id))) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def daily_stats(data: (pd.Series, pd.DataFrame), **kwargs) -> pd.DataFrame:
    """
    Daily stats for given data

    Examples:
        >>> pd.set_option('precision', 2)
        >>> (
        ...     pd.concat([
        ...         pd.read_pickle('xbbg/tests/data/sample_rms_ib0.pkl'),
        ...         pd.read_pickle('xbbg/tests/data/sample_rms_ib1.pkl'),
        ...     ], sort=False)
        ...     .pipe(get_series, col='close')
        ...     .pipe(daily_stats)
        ... )['RMS FP Equity'].iloc[:, :5]
                                   count    mean   std    min    10%
        2020-01-16 00:00:00+00:00  434.0  711.16  1.11  708.6  709.6
        2020-01-17 00:00:00+00:00  437.0  721.53  1.66  717.0  719.0
    """
    if data.empty: return pd.DataFrame()
    if 'percentiles' not in kwargs: kwargs['percentiles'] = [.1, .25, .5, .75, .9]
    return data.groupby(data.index.floor('d')).describe(**kwargs) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def simulate_walks(self, num_walks, walk_length, stay_prob=0.3, workers=1, verbose=0):

        layers_adj = pd.read_pickle(self.temp_path+'layers_adj.pkl')
        layers_alias = pd.read_pickle(self.temp_path+'layers_alias.pkl')
        layers_accept = pd.read_pickle(self.temp_path+'layers_accept.pkl')
        gamma = pd.read_pickle(self.temp_path+'gamma.pkl')
        walks = []
        initialLayer = 0

        nodes = self.idx  # list(self.g.nodes())

        results = Parallel(n_jobs=workers, verbose=verbose, )(
            delayed(self._simulate_walks)(nodes, num, walk_length, stay_prob, layers_adj, layers_accept, layers_alias, gamma) for num in
            partition_num(num_walks, workers))

        walks = list(itertools.chain(*results))
        return walks 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def test_legacy_pickle(self, datapath):
        if PY3:
            pytest.skip("testing for legacy pickles not "
                        "support on py3")

        path = datapath('indexes', 'data', 'multiindex_v1.pickle')
        obj = pd.read_pickle(path)

        obj2 = MultiIndex.from_tuples(obj.values)
        assert obj.equals(obj2)

        res = obj.get_indexer(obj)
        exp = np.arange(len(obj), dtype=np.intp)
        assert_almost_equal(res, exp)

        res = obj.get_indexer(obj2[::-1])
        exp = obj.get_indexer(obj[::-1])
        exp2 = obj2.get_indexer(obj2[::-1])
        assert_almost_equal(res, exp)
        assert_almost_equal(exp, exp2) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def test_legacy_v2_unpickle(self, datapath):

        # 0.7.3 -> 0.8.0 format manage
        path = datapath('indexes', 'data', 'mindex_073.pickle')
        obj = pd.read_pickle(path)

        obj2 = MultiIndex.from_tuples(obj.values)
        assert obj.equals(obj2)

        res = obj.get_indexer(obj)
        exp = np.arange(len(obj), dtype=np.intp)
        assert_almost_equal(res, exp)

        res = obj.get_indexer(obj2[::-1])
        exp = obj.get_indexer(obj[::-1])
        exp2 = obj2.get_indexer(obj2[::-1])
        assert_almost_equal(res, exp)
        assert_almost_equal(exp, exp2) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def test_read_explicit(self, compression, get_random_path):
        base = get_random_path
        path1 = base + ".raw"
        path2 = base + ".compressed"

        with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
            df = tm.makeDataFrame()

            # write to uncompressed file
            df.to_pickle(p1, compression=None)

            # compress
            self.compress_file(p1, p2, compression=compression)

            # read compressed file
            df2 = pd.read_pickle(p2, compression=compression)

            tm.assert_frame_equal(df, df2) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def top_k_similar_items(movies,ratings_df,k,TRAINED=False):
    """
    Returns k similar movies for respective movie
    INPUTS :
        movies : list of numbers or number, list of movie ids
        ratings_df : rating dataframe, store all users rating for respective movies
        k          : natural number
        TRAINED    : TRUE or FALSE, weather use trained user vs movie table or untrained
    OUTPUT:
        list of k similar movies for respected movie
    """
    if TRAINED:
        df=pd.read_pickle("user_item_table_train.pkl")
    else:
        df=pd.read_pickle("user_item_table.pkl")

    corr_matrix=item_item_correlation(df,TRAINED)
    if type(movies) is not list:
        return corr_matrix[movies].sort_values(ascending=False).drop(movies).index.values[0:k]
    else:
        dict={}
        for movie in movies:
            dict.update({movie:corr_matrix[movie].sort_values(ascending=False).drop(movie).index.values[0:k]})
        pd.DataFrame(dict).to_csv("movie_top_k.csv")
        return dict 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def user_user_pearson_corr(ratings_df,TRAINED):
    if TRAINED:
        if os.path.isfile("model/user_user_corr_train.pkl"):
            df_corr=pd.read_pickle("user_user_corr_train.pkl")
        else:
            df =pd.read_pickle("user_item_table_train.pkl")
            df=df.T
            df_corr=df.corr()
            df_corr.to_pickle("user_user_corr_train.pkl")
    else:
        if os.path.isfile("model/user_user_corr.pkl"):
            df_corr=pd.read_pickle("user_user_corr.pkl")
        else:
            df = pd.read_pickle("user_item_table.pkl")
            df=df.T
            df_corr=df.corr()
            df_corr.to_pickle("user_user_corr.pkl")
    return df_corr 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def pd_cache(func):
    # Caches a Pandas DF into file for later use
    # Memoization version for pandas DF
    try:
        os.mkdir('.pd_cache')
    except FileExistsError:
        pass

    @wraps(func)
    def cache(*args, **kw):
        # Get raw code of function as str and hash it
        func_code = ''.join(inspect.getsourcelines(func)[0]).encode('utf-8')
        hsh = hashlib.md5(func_code).hexdigest()[:6]
        f = '.pd_cache/' + func.__name__ + '_' + hsh + '.pkl'
        if os.path.exists(f):
            df = pd.read_pickle(f)
            return df

        # Delete any file name that has `cached_[func_name]_[6_chars]_.pkl`
        for cached in glob(f'./.pd_cache/{func.__name__}_*.pkl'):
            if (len(cached) - len(func.__name__)) == 20:
                os.remove(cached)
        # Write new
        df = func(*args, **kw)
        df.to_pickle(f)
        return df

    return cache 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def __init__(self, ticker, provider):
        # providers is a list of pricing providers
        # ex: ['alphavantage', 'Yahoo']
        self.ticker = ticker.upper()
        self.provider = provider
        self.filename = ("thewarden/pricing_engine/pricing_data/" +
                         self.ticker + "_" + provider.name + ".price")
        self.filename = os.path.join(current_path(), self.filename)
        self.errors = []
        # makesure file path exists
        os.makedirs(os.path.dirname(self.filename), exist_ok=True)
        # Try to read from file and check how recent it is
        try:
            today = datetime.now().date()
            filetime = datetime.fromtimestamp(os.path.getctime(self.filename))
            if filetime.date() == today:
                self.df = pd.read_pickle(self.filename)
            else:
                self.df = self.update_history()
        except FileNotFoundError:
            self.df = self.update_history()

        try:
            self.last_update = self.df.index.max()
            self.first_update = self.df.index.min()
            self.last_close = self.df.head(1).close[0]
        except AttributeError as e:
            self.errors.append(e)
            self.last_update = self.first_update = self.last_close = None 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_pickle [as 別名]
def update_history(self, force=False):
        # Check first if file exists and if fresh
        # The line below skips history for providers that have realtime in name
        if 'realtime' in self.provider.name:
            return None
        if not force:
            try:
                # Check if saved file is recent enough to be used
                # Local file has to have a modified time in today
                today = datetime.now().date()
                filetime = datetime.fromtimestamp(
                    os.path.getctime(self.filename))
                if filetime.date() == today:
                    price_pickle = pd.read_pickle(self.filename)
                    return (price_pickle)
            except FileNotFoundError:
                pass
        # File not found ot not new. Need to update the matrix
        # Cycle through the provider list until there's satisfactory data
        price_request = self.provider.request_data(self.ticker)
        # Parse and save
        df = self.price_parser(price_request, self.provider)
        if df is None:
            self.errors.append(
                f"Empty df for {self.ticker} using {self.provider.name}")
            return (None)
        df.sort_index(ascending=False, inplace=True)
        df.index = pd.to_datetime(df.index)
        df.to_pickle(self.filename)
        # Refresh the class - reinitialize
        return (df)