Loading .csv data using Pandas

[zaki424160]

I was trying to load a dataset from my local computer using pandas when I run the code I got these problems, please anyone help me :

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#!/usr/bin/env python   ''' An example file to show how to use the feature-selection code in ml_lib ''' import pandas from tqdm import tqdm import pandas as pd import csv from sklearn.datasets import fetch_mldata from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn import svm   import numpy as np   import seaborn as sns import matplotlib.pyplot as plt   import feature_select import depmeas   if __name__=='__main__':     NUM_CV = 3     RANDOM_SEED = 123     MAX_ITER = 1000       leuk = pd.read_csv(r'C:/Users/pc/Desktop/dataset/leukemia.csv')     X = leuk['data']     y = leuk['target']          # split the data for testing     (X_train, X_test, y_train, y_test) = train_test_split(X, y, test_size=0.3, random_state=RANDOM_SEED)       # perform feature selection     num_features_to_select = 25     K_MAX = 1000     estimator = depmeas.mi_tau     n_jobs = -1     feature_ranking = feature_select.feature_select(X_train, y_train, num_features_to_select=num_features_to_select, K_MAX=K_MAX, estimator=estimator, n_jobs=n_jobs)     num_selected_features = len(feature_ranking )     # for each feature, compute the accuracy on the test data as we add features     mean_acc = np.empty((num_selected_features,))     var_acc  = np.empty((num_selected_features,))     for ii in tqdm(range(num_selected_features), desc='Computing Classifier Performance...'):         classifier = svm.SVC(random_state=RANDOM_SEED,max_iter=MAX_ITER)         X_test_in = X_test[:,feature_ranking [0:ii+1]]         scores = cross_val_score(classifier, X_test_in, y_test, cv=NUM_CV, n_jobs=-1)           mu = scores.mean()         sigma_sq = scores.std()                   mean_acc[ii] = mu         var_acc[ii] = sigma_sq       x = np.arange(num_selected_features)+1     y = mean_acc     yLo = mean_acc-var_acc/2.     yHi = mean_acc+var_acc/2.           plt.plot(x,y)     plt.fill_between(x,yLo,yHi,alpha=0.2)     plt.grid(True)     plt.title('Leukemia Dataset Feature Selection\n Total # Features=%d' % (X.shape[1]))     plt.xlabel('# Selected Features')     plt.ylabel('SVC Classifier Accuracy')     plt.show()

Error:
Traceback (most recent call last):
  File "C:\Users\pc\PycharmProjects\MymrmrTest\venv\lib\site-packages\pandas\core\indexes\base.py", line 2657, in get_loc
    return self._engine.get_loc(key)
  File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'data'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:/Users/pc/PycharmProjects/MymrmrTest/feature_select_test.py", line 39, in <module>
    X = leuk['data']
  File "C:\Users\pc\PycharmProjects\MymrmrTest\venv\lib\site-packages\pandas\core\frame.py", line 2927, in __getitem__
    indexer = self.columns.get_loc(key)
  File "C:\Users\pc\PycharmProjects\MymrmrTest\venv\lib\site-packages\pandas\core\indexes\base.py", line 2659, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'data'

Process finished with exit code 1

[perfringo]

You should start by checking whether you have column named data in your dataframe. KeyError is probably about this row X = leuk['data']