Python pandas.read_csv方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def saveTimingInfo(summary):
    timingsPath = "test_timings.csv"
    git_version = subprocess.check_output(["git", "describe"]).strip()
    
    new_row = summary[["timing"]].T
    new_row["date"] = [datetime.datetime.now()]
    new_row["version"] = git_version


    if os.path.exists(timingsPath):
        timings = pandas.read_csv(timingsPath, index_col=0)
        timings = pandas.concat([timings, new_row])
    else:
        timings = new_row

    timings.to_csv(timingsPath)

    print(timings) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def load_label(self):
        """
        load label dictionary into the object.
        the format must be like this:
            積極,消極
            p1,n1
            p2,n2
            ...,...
            pk,nk
        """
        table=pd.read_csv(self.label_file)
        pos=table.loc[:,'積極'].tolist()
        neg=table.loc[:,'消極'].tolist()
        self.Label_index=pos+neg
        self.Label_dict=dict(zip(pos,[1]*len(pos)))
        self.Label_dict.update(dict(zip(neg,[-1]*len(neg)))) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def load_data(self, path):
        if ".pickle" in path:
            print("Loading data from: {}".format(path))
            data_utils.load_existing_data_loader(self, path)

            return True

        for split in self.data:
            file_name = "v4_atomic_{}.csv".format(map_name(split))

            df = pandas.read_csv("{}/{}".format(path, file_name), index_col=0)
            df.iloc[:, :9] = df.iloc[:, :9].apply(
                lambda col: col.apply(json.loads))

            for cat in self.categories:
                attr = df[cat]
                self.data[split]["total"] += utils.zipped_flatten(zip(
                    attr.index, ["<{}>".format(cat)] * len(attr), attr.values))

        if do_take_partial_dataset(self.opt.data):
            self.data["train"]["total"] = select_partial_dataset(
                self.opt.data, self.data["train"]["total"])

        return False 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def _load_data():

    dfTrain = pd.read_csv(config.TRAIN_FILE)
    dfTest = pd.read_csv(config.TEST_FILE)

    def preprocess(df):
        cols = [c for c in df.columns if c not in ["id", "target"]]
        df["missing_feat"] = np.sum((df[cols] == -1).values, axis=1)
        df["ps_car_13_x_ps_reg_03"] = df["ps_car_13"] * df["ps_reg_03"]
        return df

    dfTrain = preprocess(dfTrain)
    dfTest = preprocess(dfTest)

    cols = [c for c in dfTrain.columns if c not in ["id", "target"]]
    cols = [c for c in cols if (not c in config.IGNORE_COLS)]

    X_train = dfTrain[cols].values
    y_train = dfTrain["target"].values
    X_test = dfTest[cols].values
    ids_test = dfTest["id"].values
    cat_features_indices = [i for i,c in enumerate(cols) if c in config.CATEGORICAL_COLS]

    return dfTrain, dfTest, X_train, y_train, X_test, ids_test, cat_features_indices 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def gen_feat_dict(self):
        if self.dfTrain is None:
            dfTrain = pd.read_csv(self.trainfile)
        else:
            dfTrain = self.dfTrain
        if self.dfTest is None:
            dfTest = pd.read_csv(self.testfile)
        else:
            dfTest = self.dfTest
        df = pd.concat([dfTrain, dfTest])
        self.feat_dict = {}
        tc = 0
        for col in df.columns:
            if col in self.ignore_cols:
                continue
            if col in self.numeric_cols:
                # map to a single index
                self.feat_dict[col] = tc
                tc += 1
            else:
                us = df[col].unique()
                self.feat_dict[col] = dict(zip(us, range(tc, len(us)+tc)))
                tc += len(us)
        self.feat_dim = tc 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def _lsa_events_converter(events_file):
    """Make a model where each trial has its own regressor using least squares
    all (LSA)

    Parameters
    ----------
    events_file : str
        File that contains all events from the bold run

    Yields
    ------
    events : DataFrame
        A DataFrame in which each trial has its own trial_type
    """

    import pandas as pd
    events = pd.read_csv(events_file, sep='\t')
    events['original_trial_type'] = events['trial_type']
    for cond, cond_df in events.groupby('trial_type'):
        cond_idx = cond_df.index
        for i_trial, trial_idx in enumerate(cond_idx):
            trial_name = '{0}_{1:04d}'.format(cond, i_trial+1)
            events.loc[trial_idx, 'trial_type'] = trial_name
    return events 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def test_select_confounds_error(confounds_file, tmp_path):
    import pandas as pd
    import numpy as np

    confounds_df = pd.read_csv(str(confounds_file), sep='\t', na_values='n/a')

    confounds_df['white_matter'][0] = np.nan

    conf_file = tmp_path / "confounds.tsv"

    confounds_df.to_csv(str(conf_file), index=False, sep='\t', na_rep='n/a')

    with pytest.raises(ValueError) as val_err:
        _select_confounds(str(conf_file), ['white_matter', 'csf'])

    assert "The selected confounds contain nans" in str(val_err.value) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def test_select_confounds(confounds_file, selected_confounds, nan_confounds,
                          expanded_confounds):
    import pandas as pd
    import numpy as np

    confounds_df = pd.read_csv(str(confounds_file), sep='\t', na_values='n/a')

    res_df = _select_confounds(str(confounds_file), selected_confounds)

    # check if the correct columns are selected
    assert set(expanded_confounds) == set(res_df.columns)

    # check if nans are being imputed when expected
    if nan_confounds:
        for nan_c in nan_confounds:
            vals = confounds_df[nan_c].values
            expected_result = np.nanmean(vals[vals != 0])
            assert res_df[nan_c][0] == expected_result 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def load_LUT(filename, to='data'):
    '''
    load_LUT(filename) loads the given filename as a FreeSurfer LUT.

    The optional argument to (default: 'data') specifies how the LUT should be interpreted; it can
    be any of the following:
      * 'data' specifies that a dataframe should be returned.
    '''
    from neuropythy.util import to_dataframe
    import pandas
    # start by slurping in the text:
    dat = pandas.read_csv(filename, comment='#', sep='\s+', names=['id', 'name', 'r','g','b','a'])
    # if all the alpha values are 0, we set them to 1 (not sure why freesurfer does this)
    dat['a'] = 255 - dat['a']
    if pimms.is_str(to): to = to.lower()
    if   to is None: return dat
    elif to == 'data':
        df = to_dataframe({'id': dat['id'].values, 'name': dat['name'].values})
        df['color'] = dat.apply(lambda r: [r[k]/255.0 for k in ['r','g','b','a']], axis=1)
        df.set_index('id', inplace=True)
        return df
    else: raise ValueError('Unknown to argument: %s' % to)
# A function to load in default data from the freesurfer home: e.g., the default LUTs 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def main():
    data_dir_path = './data'
    model_dir_path = './models'
    ecg_data = pd.read_csv(data_dir_path + '/ecg_discord_test.csv', header=None)
    print(ecg_data.head())
    ecg_np_data = ecg_data.as_matrix()
    scaler = MinMaxScaler()
    ecg_np_data = scaler.fit_transform(ecg_np_data)
    print(ecg_np_data.shape)

    ae = BidirectionalLstmAutoEncoder()

    # fit the data and save model into model_dir_path
    if DO_TRAINING:
        ae.fit(ecg_np_data[:23, :], model_dir_path=model_dir_path, estimated_negative_sample_ratio=0.9)

    # load back the model saved in model_dir_path detect anomaly
    ae.load_model(model_dir_path)
    anomaly_information = ae.anomaly(ecg_np_data[:23, :])
    reconstruction_error = []
    for idx, (is_anomaly, dist) in enumerate(anomaly_information):
        print('# ' + str(idx) + ' is ' + ('abnormal' if is_anomaly else 'normal') + ' (dist: ' + str(dist) + ')')
        reconstruction_error.append(dist)

    visualize_reconstruction_error(reconstruction_error, ae.threshold) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def main():
    data_dir_path = './data'
    model_dir_path = './models'
    ecg_data = pd.read_csv(data_dir_path + '/ecg_discord_test.csv', header=None)
    print(ecg_data.head())
    ecg_np_data = ecg_data.as_matrix()
    scaler = MinMaxScaler()
    ecg_np_data = scaler.fit_transform(ecg_np_data)
    print(ecg_np_data.shape)

    ae = CnnLstmAutoEncoder()

    # fit the data and save model into model_dir_path
    if DO_TRAINING:
        ae.fit(ecg_np_data[:23, :], model_dir_path=model_dir_path, estimated_negative_sample_ratio=0.9)

    # load back the model saved in model_dir_path detect anomaly
    ae.load_model(model_dir_path)
    anomaly_information = ae.anomaly(ecg_np_data[:23, :])
    reconstruction_error = []
    for idx, (is_anomaly, dist) in enumerate(anomaly_information):
        print('# ' + str(idx) + ' is ' + ('abnormal' if is_anomaly else 'normal') + ' (dist: ' + str(dist) + ')')
        reconstruction_error.append(dist)

    visualize_reconstruction_error(reconstruction_error, ae.threshold) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def main():
    data_dir_path = './data'
    model_dir_path = './models'
    ecg_data = pd.read_csv(data_dir_path + '/ecg_discord_test.csv', header=None)
    print(ecg_data.head())
    ecg_np_data = ecg_data.as_matrix()
    scaler = MinMaxScaler()
    ecg_np_data = scaler.fit_transform(ecg_np_data)
    print(ecg_np_data.shape)

    ae = LstmAutoEncoder()

    # fit the data and save model into model_dir_path
    if DO_TRAINING:
        ae.fit(ecg_np_data[:23, :], model_dir_path=model_dir_path, estimated_negative_sample_ratio=0.9)

    # load back the model saved in model_dir_path detect anomaly
    ae.load_model(model_dir_path)
    anomaly_information = ae.anomaly(ecg_np_data[:23, :])
    reconstruction_error = []
    for idx, (is_anomaly, dist) in enumerate(anomaly_information):
        print('# ' + str(idx) + ' is ' + ('abnormal' if is_anomaly else 'normal') + ' (dist: ' + str(dist) + ')')
        reconstruction_error.append(dist)

    visualize_reconstruction_error(reconstruction_error, ae.threshold) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def read_names(names_path):
    """read data from downloaded file. See SmallNames.txt for example format
    or go to https://www.kaggle.com/kaggle/us-baby-names for full lists

    Args:
        names_path: path to the csv file similar to the example type
    Returns:
        Dataset: a namedtuple of two elements: deduped names and their associated
            counts. The names contain only 26 chars and are all lower case
    """
    names_data = pd.read_csv(names_path)
    names_data.Name = names_data.Name.str.lower()

    name_data = names_data.groupby(by=["Name"])["Count"].sum()
    name_counts = np.array(name_data.tolist())
    names_deduped = np.array(name_data.index.tolist())

    Dataset = collections.namedtuple('Dataset', ['Name', 'Count'])
    return Dataset(names_deduped, name_counts) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def generate_vocabulary(self, review_summary_file):
        """

        :param review_summary_file:
        :return:
        """
        self.rev_sum_pair = pd.read_csv(review_summary_file, header=0).values

        for review,summary in self.rev_sum_pair:
            rev_lst = wordpunct_tokenize(review)
            sum_lst = wordpunct_tokenize(summary)
            self.__add_list_to_dict(rev_lst)
            self.__add_list_to_dict(sum_lst)

        # Now store the "" empty string as the last word of the voacabulary
        self.map[""] = len(self.map)
        self.revmap[len(self.map)] = "" 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import read_csv [as 別名]
def get_data_from_biodbnet(self, df_hgnc):
        """keys are unique Gene names
        
        input is made of the df based on HGNC data web services

        uniprot accession are duplicated sometimes. If som this is actually the
        iprimary accession entry and all secondary ones.


        e.g. ,
        
        ABHD11 >>>> Q8N723;Q8NFV2;Q8NFV3;Q6PJU0;Q8NFV4;H7BYM8;Q8N722;Q9HBS8 ABHDB_HUMAN Alpha/beta hydrolase domain-containing protein 11
        correspond actually to the primary one : Q8NFV4

        """
        b = biodbnet.BioDBNet()
        res2 = b.db2db("Gene Symbol", ["HGNC ID", "UniProt Accession", "UniProt Entry Name", "UniProt Protein Name", "KEGG Gene ID", "Ensembl Gene ID"], 
                res.keys()[0:2000])

        import pandas as pd
        import StringIO
        c = pd.read_csv(StringIO.StringIO(res2), delimiter="\t", index_col="Gene Symbol")
        return c