Python read_csv Examples

Example #1

    def test(self):
        path = "../data/NINO3.txt"
        names = ['date', 'NINO3']
        data = read.read_csv(path, sep="\t", names=names)
        self.assertEqual(len(data.keys()), 1)

        shift = 10
        key = 'NINO3'
        data = features._shift_features(data, key, shift)
        for i in range(len(data)-shift):
            self.assertEqual(data[key][data.index[i]], data[key + '_' + str(shift)][data.index[i+shift]])

Example #2

import climatelearn.io.read as read
from climatelearn.preprocess.generating_targets import regression_set
from climatelearn.learning.regression import keras_ANN
import pandas as pd
import matplotlib.pyplot as plt

path = "../data/NINO3.txt"
NINO = read.read_csv(path, sep="\t", date_key='date_time')

path = "../data/ST_windburst.txt"
STwind = read.read_csv(path=path, sep='\t', date_key='date_time')

raw_data = pd.concat([NINO, STwind], axis=1).dropna(axis=0)

X, y = regression_set(raw_data, target_key='NINO3', initial_time=1969, horizon=1)

model = keras_ANN.KerasRegressionModel(arity=3, network_structure=(5, 1), batch_size=1, nb_epoch=1000)
model.fit(X, y)
yhat = model.predict(X)

plt.plot(range(len(yhat)), yhat, range(len(y)), y)
plt.show()

Example #3

File: test_read.py Project: yyy910805/climatelearn

 def test(self):
     path = "../data/NINO3.txt"
     names = ['date', 'NINO3']
     data = read.read_csv(path, sep="\t", names=names)
     self.assertEqual(len(data.keys()), 1)
     self.index = data.index

Example #4

File: example_classification_BIU.py Project: Ambrosys/climatelearn

import climatelearn.io.read as read
import climatelearn.clean.features as features
from climatelearn.learning.classify import classification_train as train
from climatelearn.learning.errors import confusion_matrix
import pandas as pd


path = "../data/DATA_BIU.txt"
Net_BIU = read.read_csv(path, sep="\t", date_key='date_time')

path = "../data/NINO34_BIU.txt"
nino_data = read.read_csv(path=path, sep='\t', date_key='date_time')

nino_data = nino_data.set_index(Net_BIU.index)
raw_data = pd.concat([nino_data, Net_BIU], axis=1)


data = features.classification_set(raw_data, target_key='NINO34', t0=1950.0, horizon=1.0, deltat=0.0)

print data

exit()
train_set = data[data.index < 1960]
test_set = data[data.index >= 1960]

params_weka = {
    "epochs": 10,
    "structure": [6, 6, 6],
    "batch": 0,
    "momentum": 0.05,
    "learning_rate": 0.1,

Example #5

import climatelearn.io.read as read
import climatelearn.clean.features as features
from climatelearn.learning.classify import classification_train as train
from climatelearn.learning.errors import confusion_matrix
import pandas as pd

path = "../data/DATA_BIU.txt"
Net_BIU = read.read_csv(path, sep="\t", date_key='date_time')

path = "../data/NINO34_BIU.txt"
nino_data = read.read_csv(path=path, sep='\t', date_key='date_time')

nino_data = nino_data.set_index(Net_BIU.index)
raw_data = pd.concat([nino_data, Net_BIU], axis=1)

data = features.classification_set(raw_data,
                                   target_key='NINO34',
                                   t0=1950.0,
                                   horizon=1.0,
                                   deltat=0.0)

print data

exit()
train_set = data[data.index < 1960]
test_set = data[data.index >= 1960]

params_weka = {
    "epochs": 10,
    "structure": [6, 6, 6],
    "batch": 0,

Example #6

File: test_read.py Project: Ambrosys/climatelearn

 def test(self):
     path = "../data/NINO3.txt"
     names = ["date", "NINO3"]
     data = read.read_csv(path, sep="\t", names=names)
     self.assertEqual(len(data.keys()), 1)
     self.index = data.index