path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105210810/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2, 'CompanyName', CompanyName)
cars.drop(['CarName'], axis... | code |
105210810/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2, 'CompanyName', CompanyName)
cars.drop(['CarName'], axis... | code |
105210810/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.shape
cars.columns
cars.isnull().sum()
CompanyName = cars['CarName'].apply(lambda x: x.split(' ')[0])
cars.insert(2, 'CompanyName', CompanyName)
cars.drop(['CarName'], axis... | code |
105210810/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
file_path = '../input/car-price-prediction/CarPrice_Assignment.csv'
cars = pd.read_csv(file_path, index_col='car_ID')
cars.head() | code |
48166353/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.preprocessing import image
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import matplotli... | code |
48166353/cell_6 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import xml.etree.ElementTree as ET
from PIL import Image
def change_image_channels(image, image_path):
if image.mode == 'RGBA':
r, g, b, a = image.sp... | code |
48166353/cell_11 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.preprocessing import image
from lime import lime_image
from skimage.segmentation import mark_boundaries
from sklearn.model_selection import train_tes... | code |
48166353/cell_1 | [
"text_plain_output_1.png"
] | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
#!pip install image-classifiers==0.2.2
!pip install keras_sequential_ascii
#!pip install ker... | code |
48166353/cell_8 | [
"text_plain_output_2.png",
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(5, 5), activation='relu', input_shape=(300, 400, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
mo... | code |
48166353/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
from PIL import Image
def change_image_channels(image, image_path):
if image.mode == 'RGBA':
r, g, b, a = image.split()
image = Image.merge('RGB', (r, g, b))
image.save(image_path)
eli... | code |
48166353/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.preprocessing import image
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import matplotli... | code |
48166353/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.preprocessing import image
from lime import lime_image
from skimage.segmentation import mark_boundaries
from sklearn.model_selection import train_tes... | code |
2017107/cell_9 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from sklearn.cluster import KMeans, SpectralClustering, AgglomerativeClustering
from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
2017107/cell_6 | [
"image_output_1.png"
] | from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output... | code |
2017107/cell_2 | [
"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
from subprocess import check_output
frame = pd.read_csv('../input/HR_comma_sep.csv').rename(columns={'sales': 'position'})
frame.iloc[:, 5:].head() | code |
2017107/cell_1 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
frame = pd.read_csv('../input/HR_comma_sep.csv').rename(columns={'sales': 'position'})
frame.iloc[:, :5].head() | code |
2017107/cell_7 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans, SpectralClustering, AgglomerativeClustering
from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
2017107/cell_8 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans, SpectralClustering, AgglomerativeClustering
from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
2017107/cell_3 | [
"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
from subprocess import check_output
frame = pd.read_csv('../input/HR_comma_sep.csv').rename(columns={'sales': 'position'})
pd.DataFrame({'dtypes': frame.dtypes, 'isnull': pd.isnull(frame).any(), 'count distin... | code |
2017107/cell_10 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans, SpectralClustering, AgglomerativeClustering
from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
2017107/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
def plot_dist(name, frame, color='green'):
name0 = '{}_'.format(name)
if name0 not in frame.columns:
name0 = name
data_count = len(frame[name0].unique())
if data_count > 3:
sns.distplot(frame[name0], rug=False, color=color)
if... | code |
128007514/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/list-of-world-cities-by-population-density/List of world cities by population density.csv')
def preprocess_inputs(df):
df = df.copy()
drop_cols = ['Unnamed: 0']
df = df.drop(drop_cols, axis=1)
df[A... | code |
128007514/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/list-of-world-cities-by-population-density/List of world cities by population density.csv')
data.head() | code |
18119291/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().any().sum()
train.columns[train.isnull().any()] | code |
18119291/cell_25 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_4 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
18119291/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().any().sum()
train.columns[train.isnull().any()]
missing = train.isnull().sum() / len(train)
missing = missi... | code |
18119291/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18119291/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.info() | code |
18119291/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().any().sum() | code |
18119291/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().any().sum()
train.columns[tra... | code |
18119291/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().any().sum()
train.columns[train.isnull().any()]
missing = train.isnull().sum() / len(train)
missing = missi... | code |
18119291/cell_31 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../i... | code |
18119291/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().any().sum()
train.columns[tra... | code |
18119291/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.is... | code |
18119291/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(train.shape)
print(test.shape) | code |
105187475/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.describe() | code |
105187475/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes
df.isnull()
df.isnull().any()
df.isnull().sum()
plt.xticks(fontsize=14)
for column in ... | code |
105187475/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes
df.isnull()
df.isnull().any()
df.isnull().sum() | code |
105187475/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.head() | code |
105187475/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes
df.isnull()
df.isnull().any()
df.isnull().sum()
plt.xticks(fontsize=14)
categoricalFea... | code |
105187475/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes
df.isnull()
df.isnull().any() | code |
105187475/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105187475/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape | code |
105187475/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes
df.isnull() | code |
105187475/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes
df.isnull()
df.isnull().any()
df.isnull().sum()
plt.xticks(fontsize=14)
df.info() | code |
105187475/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.info() | code |
105187475/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes
df.info() | code |
105187475/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes
df.isnull()
df.isnull().any()
df.isnull().sum()
plt.figure(figsize=(12, 10))
sns.heatma... | code |
105187475/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/concretecsv/concrete.csv')
df.shape
df.dtypes | code |
1008613/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
num_training = len(train.values)
num_testing = len(test.values)
y_train = np.array(train.pop('... | code |
1008613/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
num_training = len(train.values)
num_testing = len(test.values)
print('Amount of training data:', num_training, 'pairs of images and labels.')
print('Amount of testi... | code |
1008613/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense
from keras.models import Sequential
from keras.optimizers import Adam
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import Standa... | code |
1008613/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 warnings
warnings.filterwarnings('ignore')
import seaborn as sns
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense
from keras.optimizers... | code |
1008613/cell_19 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/t... | code |
1008613/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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.head() | code |
1008613/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense
from keras.models import Sequential
from keras.optimizers import Adam
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import Standa... | code |
1008613/cell_28 | [
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense
from keras.models import Sequential
from keras.optimizers import Adam
from sklearn.preprocessing import LabelBinarizer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train... | code |
1008613/cell_16 | [
"text_html_output_1.png"
] | from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input... | code |
1008613/cell_37 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense
from keras.models import Sequential
from keras.optimizers import Adam
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import Standa... | code |
1008613/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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
33102252/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
Ydata.corr()
Ydata[(Ydata['likes'] > 500000) & (Ydata['dislikes'] > 500000)] | code |
18127655/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
data.head() | code |
18127655/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
temp = pd.crosstab(data.USER_ID, data[c])
temp.columns = [c + '_' + st... | code |
18127655/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # Manipulación y análisis de datos, Data Frames, lectura de CSV
data = pd.read_csv('../input/pageviews/pageviews.csv', parse_dates=['FEC_EVENT'])
X_test = []
for c in data.drop(['USER_ID', 'FEC_EVENT'], axis=1).columns:
print('haciendo', c)
temp = pd.crosstab(data.USER_ID, data[c])
tem... | code |
90120122/cell_6 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
def show_image_with_filter(image, kernel):
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.expand_dims(image, axis=0)
kernel = tf.reshape(kernel, [*kernel.shape, 1, 1])
kernel = tf.cast(kernel, dtype=tf.float32)
image... | code |
32068077/cell_24 | [
"text_plain_output_1.png"
] | import cv2
import os
import random
import tensorflow
from imgaug import augmenters as iaa
import numpy as np
import time
import random
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import os
from sklearn.utils.multiclass import unique_labels
import cv2
import matplot... | code |
18109621/cell_4 | [
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
bq_assistant = BigQueryHelper('patents-public-data', 'worldbank_wdi')
bq_assistant.list_tables()
bq_assistant.head('wdi_2016', num_rows=10) | code |
18109621/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import bq_helper
import bq_helper
from bq_helper import BigQueryHelper
wdi = bq_helper.BigQueryHelper(active_project='patents-public-data', dataset_name='worldbank_wdi') | code |
18109621/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from bq_helper import BigQueryHelper
import pandas as pd
bq_assistant = BigQueryHelper('patents-public-data', 'worldbank_wdi')
bq_assistant.list_tables()
bq_assistant.table_schema('wdi_2016')
import pandas as pd
pd.get_option('max_colwidth')
pd.set_option('max_colwidth', 500)
query1 = '\nSELECT year, country_code,... | code |
18109621/cell_3 | [
"text_html_output_1.png"
] | from bq_helper import BigQueryHelper
bq_assistant = BigQueryHelper('patents-public-data', 'worldbank_wdi')
bq_assistant.list_tables() | code |
18109621/cell_14 | [
"text_plain_output_1.png"
] | import bq_helper
import bq_helper
from bq_helper import BigQueryHelper
wdi = bq_helper.BigQueryHelper(active_project='patents-public-data', dataset_name='worldbank_wdi')
query1 = '\nSELECT year, country_code,country_name, indicator_code, indicator_name, indicator_value\nFROM `patents-public-data.worldbank_wdi.wdi_2... | code |
50211059/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
50211059/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub | code |
50211059/cell_20 | [
"text_html_output_1.png"
] | from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/ha... | code |
50211059/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
50211059/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
50211059/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50211059/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
50211059/cell_18 | [
"text_html_output_1.png"
] | from sklearn.metrics.pairwise import pairwise_distances
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df... | code |
50211059/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
50211059/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
50211059/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
50211059/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd | code |
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