Python pandas.read_table方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def _assert_result(self, filename: str,
                       data: str,
                       iterations: int,
                       project_name: str,
                       result_means_filename: str,
                       debug_seed: int,
                       threshold: float,
                       result_precision: int
                       ) -> None:
        str_threshold = ''.join(str(threshold).split('.'))

        means_test_filename = \
            'statistical_analysis__{}_result__' \
            'data-{}_it-{}_seed-{}_threshold-{}_precision-{}.txt'.format(filename,
                                                                         data,
                                                                         iterations,
                                                                         debug_seed,
                                                                         str_threshold,
                                                                         result_precision)
        original_means = pd.read_table(os.path.realpath('{}/{}'.format(data_test_dir, means_test_filename)))
        result_means = pd.read_table('{}/{}/{}'.format(output_test_dir, project_name, result_means_filename))
        self.assertTrue(dataframe_functions.dataframes_has_same_data(result_means, original_means),
                        msg='failed comparing {} with {}'.format(means_test_filename, result_means_filename))
        self.remove_file('{}/{}/{}'.format(output_test_dir, project_name, result_means_filename)) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def main():
    # read and preprocess the movie data
    movie = pd.read_table('movies.dat', sep='::', names=['movie_id', 'movie_name', 'tag'], engine='python')
    movie = movie_preprocessing(movie)

    # read the ratings data and merge it with movie data
    rating = pd.read_table("ratings.dat", sep="::",
                           names=["user_id", "movie_id", "rating", "timestamp"], engine='python')
    data = pd.merge(rating, movie, on="movie_id")

    # extract feature from our data set
    streaming_batch, user_feature, actions, reward_list = feature_extraction(data)
    streaming_batch.to_csv("streaming_batch.csv", sep='\t', index=False)
    user_feature.to_csv("user_feature.csv", sep='\t')
    pd.DataFrame(actions, columns=['movie_id']).to_csv("actions.csv", sep='\t', index=False)
    reward_list.to_csv("reward_list.csv", sep='\t', index=False)

    action_context = movie[movie['movie_id'].isin(actions)]
    action_context.to_csv("action_context.csv", sep='\t', index = False) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def get_ref_contig_sizes(altref_file):
    """
    Get a Series of contigs lengths. Includes primary and alt contigs.

    :param altref_file: BED file of contig information where each record spans the whole contig. Must contain
        columns "#CHROM" and "END".

    :return: Series of contig lengths indexed by the contig name.
    """

    # Get reference chromosome sizes
    ref_len_series = pd.read_table(altref_file, header=0)
    ref_len_series.index = ref_len_series['#CHROM']
    ref_len_series = ref_len_series['END']

    return ref_len_series 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def _read_dataframe(filename):
    """ Reads the original dataset TSV as a pandas dataframe """
    # delay importing this to avoid another dependency
    import pandas

    # read in triples of user/artist/playcount from the input dataset
    # get a model based off the input params
    start = time.time()
    log.debug("reading data from %s", filename)
    data = pandas.read_table(filename,
                             usecols=[0, 2, 3],
                             names=['user', 'artist', 'plays'],
                             na_filter=False)

    # map each artist and user to a unique numeric value
    data['user'] = data['user'].astype("category")
    data['artist'] = data['artist'].astype("category")

    # store as a CSR matrix
    log.debug("read data file in %s", time.time() - start)

    return data 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def _read_dataframe(filename):
    """ Reads the original dataset TSV as a pandas dataframe """
    # delay importing this to avoid another dependency
    import pandas

    # read in triples of user/artist/playcount from the input dataset
    # get a model based off the input params
    start = time.time()
    log.debug("reading data from %s", filename)
    data = pandas.read_table(filename, usecols=[0, 1, 3], names=['user', 'item', 'rating'])

    # map each artist and user to a unique numeric value
    data['user'] = data['user'].astype("category")
    data['item'] = data['item'].astype("category")

    # store as a CSR matrix
    log.debug("read data file in %s", time.time() - start)
    return data 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def _read_triplets_dataframe(filename):
    """ Reads the original dataset TSV as a pandas dataframe """
    # delay importing this to avoid another dependency
    import pandas

    # read in triples of user/artist/playcount from the input dataset
    # get a model based off the input params
    start = time.time()
    log.debug("reading data from %s", filename)
    data = pandas.read_table("train_triplets.txt", names=['user', 'track', 'plays'])

    # map each artist and user to a unique numeric value
    data['user'] = data['user'].astype("category")
    data['track'] = data['track'].astype("category")

    # store as a CSR matrix
    log.debug("read data file in %s", time.time() - start)

    return data 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def test_1000_sep(self):
        data = """A|B|C
1|2,334|5
10|13|10.
"""
        expected = DataFrame({
            'A': [1, 10],
            'B': [2334, 13],
            'C': [5, 10.]
        })

        df = self.read_csv(StringIO(data), sep='|', thousands=',')
        tm.assert_frame_equal(df, expected)

        df = self.read_table(StringIO(data), sep='|', thousands=',')
        tm.assert_frame_equal(df, expected) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def test_duplicate_columns(self):
        for engine in ['python', 'c']:
            data = """A,A,B,B,B
    1,2,3,4,5
    6,7,8,9,10
    11,12,13,14,15
    """
            # check default beahviour
            df = self.read_table(StringIO(data), sep=',',engine=engine)
            self.assertEqual(list(df.columns), ['A', 'A.1', 'B', 'B.1', 'B.2'])

            df = self.read_table(StringIO(data), sep=',',engine=engine,mangle_dupe_cols=False)
            self.assertEqual(list(df.columns), ['A', 'A', 'B', 'B', 'B'])

            df = self.read_table(StringIO(data), sep=',',engine=engine,mangle_dupe_cols=True)
            self.assertEqual(list(df.columns), ['A', 'A.1', 'B', 'B.1', 'B.2']) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def test_no_header(self):
        data = """1,2,3,4,5
6,7,8,9,10
11,12,13,14,15
"""
        df = self.read_table(StringIO(data), sep=',', header=None)
        df_pref = self.read_table(StringIO(data), sep=',', prefix='X',
                                  header=None)

        names = ['foo', 'bar', 'baz', 'quux', 'panda']
        df2 = self.read_table(StringIO(data), sep=',', names=names)
        expected = [[1, 2, 3, 4, 5.],
                    [6, 7, 8, 9, 10],
                    [11, 12, 13, 14, 15]]
        tm.assert_almost_equal(df.values, expected)
        tm.assert_almost_equal(df.values, df2.values)

        self.assert_(np.array_equal(df_pref.columns,
                                    ['X0', 'X1', 'X2', 'X3', 'X4']))
        self.assert_(np.array_equal(df.columns, lrange(5)))

        self.assert_(np.array_equal(df2.columns, names)) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def test_1000_sep_with_decimal(self):
        data = """A|B|C
1|2,334.01|5
10|13|10.
"""

        expected = DataFrame({
            'A': [1, 10],
            'B': [2334.01, 13],
            'C': [5, 10.]
        })

        df = self.read_csv(StringIO(data), sep='|', thousands=',')
        tm.assert_frame_equal(df, expected)

        df = self.read_table(StringIO(data), sep='|', thousands=',')
        tm.assert_frame_equal(df, expected) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def test_iteration_open_handle(self):
        if PY3:
            raise nose.SkipTest("won't work in Python 3 {0}".format(sys.version_info))

        with tm.ensure_clean() as path:
            with open(path, 'wb') as f:
                f.write('AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG')

            with open(path, 'rb') as f:
                for line in f:
                    if 'CCC' in line:
                        break

                try:
                    read_table(f, squeeze=True, header=None, engine='c')
                except Exception:
                    pass
                else:
                    raise ValueError('this should not happen')

                result = read_table(f, squeeze=True, header=None,
                                    engine='python')

                expected = Series(['DDD', 'EEE', 'FFF', 'GGG'])
                tm.assert_series_equal(result, expected) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def merge_files(fl_lst, output, ext):


    df_lst = []
    for fl in fl_lst:
        df = pd.read_table(fl, sep='\t', index_col=0, header=0)

        old_header = df.columns.values
        new_header = [os.path.basename(fl).split(".")[0]+"_"+col_id for col_id in old_header]
        df.rename(columns=dict(zip(old_header, new_header)), inplace=True)

        df_lst.append(df)

    merged_dfs = pd.concat(df_lst, axis=1)

    header = merged_dfs.columns.values

    with open("%s.%s" % (output, ext), "w+") as fh:
            ln = "\t".join(header)
            fh.write(ln+"\n")

    with open("%s.%s" % (output, ext), "a") as fh:
            merged_dfs.to_csv(fh, sep="\t", na_rep="nan", header=False) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def annotate(anno_file, chromo, pos):
    #anno_file = dat.GzipFile(anno_file, 'r')
    anno_file = get_fh(anno_file, 'r')
    anno = pd.read_table(anno_file, header=None, usecols=[0, 1, 2],
                         dtype={0: 'str', 1: 'int32', 2: 'int32'})
    anno_file.close()
    anno.columns = ['chromo', 'start', 'end']
    anno.chromo = anno.chromo.str.upper().str.replace('CHR', '')
    anno = anno.loc[anno.chromo == chromo]
    anno.sort_values('start', inplace=True)
    start, end = an.join_overlapping(anno.start.values, anno.end.values)
    anno = np.array(an.is_in(pos, start, end), dtype='int8')
    return anno 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def get_datasets(fpath, condition=None):
    unit = 0
    datasets = []
    for root, dir, files in os.walk(fpath):
        if 'log.txt' in files:
            param_path = open(os.path.join(root,'params.json'))
            params = json.load(param_path)
            exp_name = params['exp_name']
            
            log_path = os.path.join(root,'log.txt')
            experiment_data = pd.read_table(log_path)

            experiment_data.insert(
                len(experiment_data.columns),
                'Unit',
                unit
                )        
            experiment_data.insert(
                len(experiment_data.columns),
                'Condition',
                condition or exp_name
                )

            datasets.append(experiment_data)
            unit += 1

    return datasets 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_table [as 別名]
def get_datasets(fpath, condition=None):
    unit = 0
    datasets = []
    for root, dir, files in os.walk(fpath):
        if 'log.txt' in files:
            param_path = open(os.path.join(root,'params.json'))
            params = json.load(param_path)
            exp_name = params['exp_name']
            
            log_path = os.path.join(root,'log.txt')
            experiment_data = pd.read_table(log_path)

            experiment_data.insert(
                len(experiment_data.columns),
                'Unit',
                unit
                )        
            experiment_data.insert(
                len(experiment_data.columns),
                'Condition',
                condition or exp_name
                )

            datasets.append(experiment_data)
            unit += 1
    datasets = pd.concat(datasets, ignore_index=True)
    return datasets