path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129005548/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.describe() | code |
16109895/cell_63 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_21 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_13 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_25 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
vector_row = np.array([1, 2, 3])
vector_row | code |
16109895/cell_57 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_56 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_34 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_23 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_30 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_33 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_44 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_20 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_55 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
vector_col | code |
16109895/cell_40 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_29 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_39 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_26 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_48 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_61 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_54 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
type(matrix_object) | code |
16109895/cell_60 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_19 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_50 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_52 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_45 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_49 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_51 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_62 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_59 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_58 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_28 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix | code |
16109895/cell_15 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_16 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_38 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_47 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_17 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_35 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_43 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_31 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_46 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_24 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_22 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_53 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
matrix_object | code |
16109895/cell_27 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
16109895/cell_36 | [
"text_plain_output_1.png"
] | from scipy import sparse
import numpy as np
vector_row = np.array([1, 2, 3])
vector_col = np.array([[1], [2], [3]])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
matrix_object = np.mat([[1, 2], [3, 4]])
from scipy import sparse
matrix = np.array([[1, 0, 2, 0], [1, 0, 0, 1], [0, 0, 1, 0]])
matrix_sparse = sparse.csr_m... | code |
33105736/cell_6 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(im... | code |
33105736/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py
!python pytorch-xla-env-setup.py --apt-packages libomp5 libopenblas-dev | code |
33105736/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in c... | code |
33105736/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim a... | code |
33105736/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim a... | code |
33105736/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim a... | code |
33105736/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(im... | code |
16119155/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
df = pd.read_csv('../input/rural_urban.csv')
df = df.drop(df.index[:7])
df.groupby('area')['transgender'].agg('sum').sort_values(ascending=False).head(10).plot(kind='bar')
df... | code |
16119155/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
df = pd.read_csv('../input/rural_urban.csv')
df.head(10) | code |
16119155/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
df = pd.read_csv('../input/rural_urban.csv')
df = df.drop(df.index[:7])
df.groupby('area')['transgender'].agg('sum').sort_values(ascending=False).head(10).plot(kind='bar')
df... | code |
16119155/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
df = pd.read_csv('../input/rural_urban.csv')
df = df.drop(df.index[:7])
df.head() | code |
16119155/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
df = pd.read_csv('../input/rural_urban.csv')
df = df.drop(df.index[:7])
df.groupby('area')['transgender'].agg('sum').sort_values(ascending=False).head(10).plot(kind='bar') | code |
89126682/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
lr =... | code |
89126682/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
plt.figure(figsize=(10, 8))
sns.histplot(df['Salary'], kde=True, bins=40)
plt.show() | code |
89126682/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
df.head() | code |
89126682/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-a... | code |
89126682/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
lr = LogisticRegression(random_state=0)
lr.fit(X_train, y_train)
y_pred_train = lr.predict(X_train)
y_pred_train
y_pred_test = lr.predict(X_test)
print(classificat... | code |
89126682/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
lr = LogisticRegression(random_state=0)
lr.fit(X_train, y_train)
y_pred_train = lr.predict(X_train)
y_pred_train
linR = LinearRegression().fit(X_train, y_train)
y_p... | code |
89126682/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
lr =... | code |
89126682/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-a... | code |
89126682/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
lr = LogisticRegression(random_state=0)
lr.fit(X_train, y_train)
y_pred_train = lr.predict(X_train)
y_pred_train
linR = LinearRegression().fit(X_train, y_train)
y_pred_train = linR.predict(X_train)
sum(y_pred_train) / len(y_pred_train) | code |
89126682/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
df.columns | code |
89126682/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
lr =... | code |
89126682/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
clicked = df[df['Clicked'] == 1]
no_clicked = df[df['Clicked'] == 0]
print('Total=', len(df))
print('Number of customers clicked = ', len(clicked))
print('Number of customers not clicked = ', len(no_clicked)) | code |
89126682/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
lr = LogisticRegression(random_state=0)
lr.fit(X_train, y_train)
y_pred_train = lr.predict(X_train)
y_pred_train | code |
89126682/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
lr =... | code |
89126682/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
lr = LogisticRegression(random_state=0)
lr.fit(X_train, y_train)
y_pred_train = lr.predict(X_train)
y_pred_train
linR = LinearRegression().fit(X_train, y_train)
y_pred_train = linR.predict(X_train)
sum(y_pred_train) / len(y_pred_train) | code |
89126682/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
sns.scatterplot(data=df, x=df['Time Spent on Site'], y=df['Salary'], hue=df['Clicked'])
plt.show() | code |
89126682/cell_17 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
lr = LogisticRegression(random_state=0)
lr.fit(X_train, y_train) | code |
89126682/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-a... | code |
89126682/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
lr =... | code |
89126682/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/facebook-ads/Facebook_Ads_2.csv', encoding='ISO-8859-1')
df.columns
df = df.drop(['Names', 'emails'], axis=1)
df = df.drop(['Country'], axis=1)
df.head() | code |
89126682/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
lr = LogisticRegression(random_state=0)
lr.fit(X_train, y_train)
y_pred_train = lr.predict(X_train)
y_pred_train
y_pred_test = lr.predict(X_test)
print('Accuracy: ... | code |
89126682/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
lr = LogisticRegression(random_state=0)
lr.fit(X_train, y_train)
y_pred_train = lr.predict(X_train)
y_pred_train
linR = LinearRegression().fit(X_train, y_train)
y_pred_train = linR.predict(X_train)
for i in range(0, len(y_pred_train)):
if y_pr... | code |
89126682/cell_37 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression,LinearRegression
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/facebook-a... | code |
311174/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations repor... | code |
311174/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sbn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
311174/cell_3 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.head(3) | code |
311174/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df[df.data_field == 'confirmed_male'].value.plot()
df[df.data_field == 'confirmed_female'].value.plot(... | code |
2008393/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
sales = pd.read_csv('../input/transactions.csv', parse_dates=['date'], dtype={'store_nbr': np.uint8, 'transactions': np.uint16})
items = pd.read_csv('../input/items.csv', dtype={'item_nbr': np.uint32, 'class': np.uint16, 'perishable': np.bool})
stores = pd.read_csv('../input/sto... | code |
2008393/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import math
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/transactions.csv', parse_dates=['date'], dtype={'store_nbr': np.uint8, 'transactions': np.uint16})
items = pd.read_csv('../input/items.csv', dtype={'item_nbr': np.uint32, 'class': np.uint16, 'perishable': np.bool})
stores = pd.read_csv(... | code |
2008393/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import math
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/transactions.csv', parse_dates=['date'], dtype={'store_nbr': np.uint8, 'transactions': np.uint16})
items = pd.read_csv('../input/items.csv', dtype={'item_nbr': np.uint32, 'class': np.uint16, 'perishable': np.bool})
stores = pd.read_csv(... | code |
2008393/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import math
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/transactions.csv', parse_dates=['date'], dtype={'store_nbr': np.uint8, 'transactions': np.uint16})
items = pd.read_csv('../input/items.csv', dtype={'item_nbr': np.uint32, 'class': np.uint16, 'perishable': np.bool})
stores = pd.read_csv(... | code |
2008393/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import math
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/transactions.csv', parse_dates=['date'], dtype={'store_nbr': np.uint8, 'transactions': np.uint16})
items = pd.read_csv('../input/items.csv', dtype={'item_nbr': np.uint32, 'class': np.uint16, 'perishable': np.bool})
stores = pd.read_csv(... | code |
2008393/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import math
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/transactions.csv', parse_dates=['date'], dtype={'store_nbr': np.uint8, 'transactions': np.uint16})
items = pd.read_csv('../input/items.csv', dtype={'item_nbr': np.uint32, 'class': np.uint16, 'perishable': np.bool})
stores = pd.read_csv(... | code |
2008393/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import math
import numpy as np
import pandas as pd
sales = pd.read_csv('../input/transactions.csv', parse_dates=['date'], dtype={'store_nbr': np.uint8, 'transactions': np.uint16})
items = pd.read_csv('../input/items.csv', dtype={'item_nbr': np.uint32, 'class': np.uint16, 'perishable': np.bool})
stores = pd.read_csv(... | code |
128010282/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
Data.corr()
feature_name = list(Data.columns[:-1])
Data.drop('NOX', ax... | code |
128010282/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
Data.corr()
sns.lineplot(x=Data['NOX'], y=Data['MEDV'], c='r') | code |
128010282/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum() | code |
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