Note

How to find if a given array has any null values? # Input url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' iris_2d = np.genfromtxt(url, delimiter=',', dtype='float', usecols=[0,1,2,3]) np.isnan(iris_2d).any() # output False

How to find the correlation between two columns of a numpy array? # Input url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' iris = np.genfromtxt(url, delimiter=',', dtype='float', usecols=[0,1,2,3]) # 1 np.corrcoef(iris[:, 0], iris[:, 2])[0, 1] # 2 from scipy.stats.stats import pearsonr corr, p_value = pearsonr(iris[:, 0], iris[:, 2]) print(corr) # output 0.8717541..

How to drop rows that contain a missing value from a numpy array? # Input url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' iris_2d = np.genfromtxt(url, delimiter=',', dtype='float', usecols=[0,1,2,3]) iris_2d[np.random.randint(150, size=20), np.random.randint(4, size=20)] = np.nan # Solution # No direct numpy function for this. # 1: any_nan_in_row = np.array([~np...

How to filter a numpy array based on two or more conditions? # Input url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' iris_2d = np.genfromtxt(url, delimiter=',', dtype='float', usecols=[0,1,2,3]) # Solution condition = (iris_2d[:, 2] > 1.5) & (iris_2d[:, 0] < 5.0) iris_2d[condition] # output array([[ 4.8, 3.4, 1.6, 0.2], [ 4.8, 3.4, 1.9, 0.2], [ 4.7, 3.2, 1.6, 0.2..

How to find the position of missing values in numpy array? # Input url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' iris_2d = np.genfromtxt(url, delimiter=',', dtype='float', usecols=[0,1,2,3]) iris_2d[np.random.randint(150, size=20), np.random.randint(4, size=20)] = np.nan # Solution print("Number of missing values: \n", np.isnan(iris_2d[:, 0]).sum()) # output 5 ..