ValueError: Found array with 0 feature(s) (shape=(54, 0)) while a minimum of 1 is required

Question

I want to perform PCA on a list of dataframes, such as cna. My code raised ValueError: Found array with 0 feature(s) (shape=(54, 0)) while a minimum of 1 is required.

class StringConverter(dict):
    def __contains__(self, item):
        return True

    def __getitem__(self, item):
        return str

    def get(self, default=None):
        return str


def get_pathways():
    pathways = []

    with open(dir + "hsa.txt", newline='') as csvfile:
        freader = csv.reader(csvfile, delimiter='\t')

        for row in freader:
            pathways.append(row)
    return pathways


class DataProcessing:

    def __init__(self, data, header=0):
        self.df = pd.read_csv(data, sep="\t", header=header)

    def split_data(self):
        X = self.df.iloc[:, :-1]
        y = self.df.iloc[:, -1]
        return X, y

    def pca(self):
        pca = PCA()
        if np.any(np.isnan(self.df)):
            pass
        elif np.all(np.isfinite(self.df)):
            pass
        else:
            pca.fit(self.df.iloc[1:, 3:])
            self.pca_components = pca.components_
            return self.pca_components


def main():
    cna = DataProcessing(dir + "data_linear_cna.txt")
    patients_source = DataProcessing(dir + "data_clinical_patient_reduced.txt", 0)

    patients = {}
    # get list of allowed patients
    for index, row in patients_source.df.iterrows():
        if row.OS_MONTHS != '[Not Available]':
            if float(row.OS_MONTHS) > 2 * 12:
                patients[row.PATIENT_ID] = 1

    pathways = get_pathways()

    #### Process the CNA data
    q = 5
    C = []
    G = []
    M = []

    # find common samples
    n = cna.df.shape[1]
    common_samples = {}

    cna_sample_index = {}

    for i in range(2, n):
        sample_name = list(cna.df)[i][0:-3]
        if sample_name in patients:
            common_samples[sample_name] = 1
            cna_sample_index[sample_name] = i

    ordered_common_samples = list(common_samples.keys())
    ordered_common_samples.sort()
            
    process_cna = True

    if process_cna:
        C = []

        for i, p in enumerate(pathways):
            allowed_symbols = {}
            first = True
            for s in p:
                if first:
                    first = False
                else:
                    if s != 'NA':
                        allowed_symbols[s] = 1
            # going through each sample
            B = []
            allowed_columns = []
            for s in ordered_common_samples:
                B.append([])
                allowed_columns.append(cna_sample_index[s])

            Bi = 0
            for index, row in cna.df.iterrows():
                if row[0].upper() in allowed_symbols:
                    Bi2 = Bi
                    for c in allowed_columns:
                        B[Bi2].append(cna.df.iloc[index, c])
                        Bi2 = Bi2 + 1
            pca_B = PCA()
            pca_B.fit(B)

            C.append(pca_B.transform(B)[:, 0:q])

    cna.pca()

main()

Traceback:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-59-d61a65bf4d9e> in <module>()
    248 
    249 
--> 250 main()

4 frames
/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    815                 "Found array with %d feature(s) (shape=%s) while"
    816                 " a minimum of %d is required%s."
--> 817                 % (n_features, array.shape, ensure_min_features, context)
    818             )
    819 

ValueError: Found array with 0 feature(s) (shape=(54, 0)) while a minimum of 1 is required.