path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32068245/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/sinan-dataset/multiple_linear_regression_dataset.csv', sep=';')
data
from sklearn.linear_model import LinearRegression
linear_reg... | code |
32068245/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/sinan-dataset/multiple_linear_regression_dataset.csv', sep=';')
data
from sklearn.linear_model import LinearRegression
linear_reg... | code |
32068245/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/sinan-dataset/multiple_linear_regression_dataset.csv', sep=';')
data
fro... | code |
32068245/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/sinan-dataset/multiple_linear_regression_dataset.csv', sep=';')
data
plt.scatter(data.deneyim, data.maas)
plt.xlabel('deneyim')
plt.ylabel('maas')
plt.show() | code |
128037874/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_9 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_index = []
coasts_df = coasts
for i in... | code |
128037874/cell_25 | [
"text_plain_output_1.png"
] | import shutil
import shutil
shutil.make_archive('coast_images', 'zip', 'coast_images')
shutil.make_archive('coast_labels', 'zip', 'coast_labels')
shutil.make_archive('coast_info', 'zip', 'coast_info') | code |
128037874/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.describe() | code |
128037874/cell_23 | [
"image_output_1.png"
] | !mkdir coast_images
!mkdir coast_labels
!mkdir coast_info | code |
128037874/cell_6 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0] | code |
128037874/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape | code |
128037874/cell_11 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from PIL import Image
import matplotlib.pyplot as plt
import os
"\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n" | code |
128037874/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_index = []
coasts_df = coasts
print(co... | code |
128037874/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_index = []
coasts_df = coasts
for i in... | code |
128037874/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_16 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.head() | code |
128037874/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_24 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coa... | code |
128037874/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.v... | code |
128037874/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_12 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts()
coasts.iloc[0]
class_set = set()
class_i... | code |
128037874/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_dir = '/kaggle/input/coast-data'
coasts = pd.read_csv(os.path.join(base_dir, 'CoastTrain_imagery_details.csv'))
coasts.shape
coasts.name.value_counts() | code |
106202263/cell_13 | [
"text_plain_output_1.png"
] | another_list = [5, True, 'tree', 'tree']
print(another_list) | code |
106202263/cell_15 | [
"text_plain_output_1.png"
] | mylist = ['banana', 'cherry', 'apple']
print(mylist)
print(mylist[0])
print(mylist[1])
print(mylist[2])
print(mylist[-1])
print(mylist[-2]) | code |
106202263/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | mylist = ['banana', 'cherry', 'apple']
print(mylist[4]) | code |
106202263/cell_10 | [
"text_plain_output_1.png"
] | mylist = ['banana', 'cherry', 'apple']
print(mylist) | code |
106202263/cell_12 | [
"text_plain_output_1.png"
] | newlist = list()
print(newlist) | code |
17115822/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
fig = plt.figure(figsize = (30,20))
ax = fig.gca()
hist = data.hist(ax=ax)
data['Sex'] = dat... | code |
17115822/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)
data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
fig = plt.figure(figsize=(30, 20))
ax = fig.gca()
hist = data.hist(ax=ax) | code |
17115822/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_s... | code |
17115822/cell_26 | [
"image_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_s... | code |
17115822/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
fig = plt.figure(figsize = (30,20))
ax = fig.gca()
hist = data.hist(ax=ax)
data['Sex'] = data['Sex'].map({'female': 1, 'male': ... | code |
17115822/cell_19 | [
"text_html_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
import matplotlib.pyplot as plt
... | code |
17115822/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn
import os
print(os.listdir('../input')) | code |
17115822/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
fig = plt.figure(figsize = (30,20))
ax = fig.gca()
hist = data.hist(ax=ax)
data['Cabin'].head() | code |
17115822/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_s... | code |
17115822/cell_28 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_s... | code |
17115822/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
fig = plt.figure(figsize = (30,20))
ax = fig.gca()
hist = data.hist(ax=ax)
data['Sex'] = data['Sex'].map({'female': 1, 'male': ... | code |
17115822/cell_15 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
fig = plt.figure(figsize = (30,20))
ax = fig.gca()
hist = data.hist(ax=ax)
data['Sex'] = dat... | code |
17115822/cell_3 | [
"text_plain_output_2.png",
"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('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
data.head()
test_data.head() | code |
17115822/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
fig = plt.figure(figsize = (30,20))
ax = fig.gca()
hist = data.hist(ax=ax)... | code |
17115822/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_s... | code |
17115822/cell_22 | [
"text_html_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighbo... | code |
17115822/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)
data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
fig = plt.figure(figsize = (30,20))
ax = fig.gca()
hist = data.hist(ax=ax)
data.describe() | code |
88101152/cell_4 | [
"text_plain_output_1.png"
] | import math
class Panorama:
"""Class that represents a picture returned by Google Street View Static API"""
def __init__(self, width=640, height=640, fov=120, heading=0, pitch=0):
self.width = width
self.height = height
self.fov = fov
self.heading = heading
self.pitch = ... | code |
122251788/cell_13 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from PIL import ImageFile
from keras.applications import vgg16
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix
import numpy as np # l... | code |
122251788/cell_9 | [
"image_output_1.png"
] | from PIL import ImageFile
from keras.applications import vgg16
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
train_path = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_path =... | code |
122251788/cell_4 | [
"text_plain_output_1.png"
] | from PIL import ImageFile
from keras.preprocessing.image import ImageDataGenerator
train_path = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_path = '../input/yoga-poses-dataset/DATASET/TEST'
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1 / 255.0, rotation_ra... | code |
122251788/cell_11 | [
"text_plain_output_1.png"
] | from PIL import ImageFile
from keras.applications import vgg16
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
train_path = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_path =... | code |
122251788/cell_1 | [
"text_plain_output_1.png"
] | from os import walk
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
import os
from os import walk
for dirpath, dirnames, filenames in walk('../input/yoga-poses-dataset/DATASET'):
print('Directory path: ', dirpath) | code |
122251788/cell_7 | [
"text_plain_output_1.png"
] | from PIL import ImageFile
from keras.applications import vgg16
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
train_path = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_path = '../input/yoga-poses-dataset/DATAS... | code |
122251788/cell_15 | [
"text_plain_output_1.png"
] | from PIL import ImageFile
from keras.applications import vgg16
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix
import matplotlib.pypl... | code |
122251788/cell_14 | [
"image_output_1.png"
] | from PIL import ImageFile
from keras.applications import vgg16
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix
import numpy as np # l... | code |
122251788/cell_10 | [
"text_plain_output_1.png"
] | from PIL import ImageFile
from keras.applications import vgg16
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import pandas as pd # data processi... | code |
122251788/cell_12 | [
"text_plain_output_1.png"
] | from PIL import ImageFile
from keras.applications import vgg16
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
train_path = '../input/yoga-pos... | code |
122251788/cell_5 | [
"text_plain_output_1.png"
] | from keras.applications import vgg16
from keras.applications import vgg16
base_model = vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3), pooling='max') | code |
89126900/cell_4 | [
"text_plain_output_1.png"
] | import datetime
import pandas as pd
import numpy as np
import pandas as pd
import re
import datetime
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
input_dir = '/kaggle/input/tabular-playground-series-mar-2022/'
def handle_dates(df):
df['datetime'] = pd.to_datetime(df['time'])
... | code |
89126900/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import datetime
import pandas as pd
import numpy as np
import pandas as pd
import re
import datetime
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
input_dir = '/kaggle/input/tabular-playground-series-mar-2022/'
def handle_dates(df):
df['datetime'] = pd.to_datetime(df['time'])
... | code |
50224683/cell_13 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50224683/cell_4 | [
"text_plain_output_1.png"
] | import zipfile
def extract_files(source_path, target_path):
zip_ref = zipfile.ZipFile(source_path, 'r')
zip_ref.extractall(target_path)
zip_ref.close()
extract_files('/kaggle/input/dogs-vs-cats/test1.zip', '/kaggle/working/')
extract_files('/kaggle/input/dogs-vs-cats/train.zip', '/kaggle/working/') | code |
50224683/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
print(os.listdir('../input/dogs-vs-cats')) | code |
50224683/cell_18 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50224683/cell_8 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import random
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_W... | code |
50224683/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50224683/cell_17 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50224683/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import numpy as np
import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
IMAGE_WIDTH = 128
IMAGE_HEIGHT = 12... | code |
50224683/cell_5 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import os
filenames = os.listdir('/kaggle/working/train')
filenames | code |
2001025/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f:
for line in f.readlines():
x = line.split()
if x[0] == 'atom':
pos_data.append([np.array(x[1:4], dtype=np.float), x[4]])
elif... | code |
18100300/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data) | code |
18100300/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label) | code |
33099888/cell_13 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import requests
import unidecode
INPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
INPUT_DIR = '../input'
OUTPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
OUTPUT_DIR = '../output'
URL_OFFICIAL_DATASET = 'https://www.datos.gov.co/api/vie... | code |
33099888/cell_11 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import requests
INPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
INPUT_DIR = '../input'
OUTPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
OUTPUT_DIR = '../output'
URL_OFFICIAL_DATASET = 'https://www.datos.gov.co/api/views/gt2j-8ykr/rows.... | code |
33099888/cell_15 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import requests
import unidecode
INPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
INPUT_DIR = '../input'
OUTPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
OUTPUT_DIR = '../output'
URL_OFFICIAL_DATASET = 'https://www.datos.gov.co/api/vie... | code |
33099888/cell_16 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import requests
import unidecode
INPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
INPUT_DIR = '../input'
OUTPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
OUTPUT_DIR = '../output'
URL_OFFICIAL_DATASET = 'https://www.datos.gov.co/api/vie... | code |
33099888/cell_14 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import requests
import unidecode
INPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
INPUT_DIR = '../input'
OUTPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
OUTPUT_DIR = '../output'
URL_OFFICIAL_DATASET = 'https://www.datos.gov.co/api/vie... | code |
33099888/cell_10 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import requests
INPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
INPUT_DIR = '../input'
OUTPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
OUTPUT_DIR = '../output'
URL_OFFICIAL_DATASET = 'https://www.datos.gov.co/api/views/gt2j-8ykr/rows.... | code |
33099888/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import requests
INPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
INPUT_DIR = '../input'
OUTPUT_DIR = './'
if os.path.split(os.path.abspath('.'))[-1] == 'src':
OUTPUT_DIR = '../output'
URL_OFFICIAL_DATASET = 'https://www.datos.gov.co/api/views/gt2j-8ykr/rows.... | code |
16115465/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_original = pd.read_csv('../input/train.csv')
structures_original = pd.read_csv('../input/structures.csv')
test_original = pd.read_csv('../input/test.csv')
tmp_merge = pd.DataFrame.merge(train_original, structures_original, how='left', left_o... | code |
16115465/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16115465/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_original = pd.read_csv('../input/train.csv')
structures_original = pd.read_csv('../input/structures.csv')
test_original = pd.read_csv('../input/test.csv')
tmp_merge = pd.DataFrame.merge(train_original, structures_original, how='left', left_o... | code |
32062535/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
pd.set_option('display.max_columns', 1000)
pd.set_option('display.max_rows', 1000)
train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv', parse_dates=['Date'])
test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv', parse_dates=['Date'])
c... | code |
32062535/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
pd.set_option('display.max_columns', 1000)
pd.set_option('display.max_rows', 1000)
train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv', parse_dates=['Date'])
test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv', parse_dates=['Date'])
c... | code |
50240297/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
main_df.info() | code |
50240297/cell_23 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
final_sx_df.plot(kind='bar', rot=90, color='#FF0000')
plt.xlabel('Survival % based on sex')
plt.ylabel('% of Survived')
plt.title('Impact of sex of a passenger on their survival rate on Titanic')
plt.show() | code |
50240297/cell_30 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_20 | [
"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)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
print('Files Imported!') | code |
50240297/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
50240297/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 |
50240297/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file_path = '../input/titanic/train.csv'
gs_file_path = '../input/titanic/gender_submission.csv'
main_df = pd.read_csv(train_file_path)
gender_sub_df = pd.read_csv(gs_file_path)
cols1 = main_df.columns.to_list()
cols2 = gender_sub_df.columns... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.