path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2007984/cell_2 | [
"application_vnd.jupyter.stderr_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')
print(train.shape)
train.head() | code |
2007984/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | 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')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float... | code |
2007984/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Lambda, Flatten
from keras.optimizers import Adam, RMSprop
from sklearn.model_selection import train_test_split
from subpro... | code |
2007984/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | 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')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float... | code |
2007984/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear a... | code |
2007984/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
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('../inp... | code |
2007984/cell_8 | [
"application_vnd.jupyter.stderr_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')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
x_train = x_trai... | code |
2007984/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.utils.np_utils import to_categorical
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')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.a... | code |
2007984/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.utils.np_utils import to_categorical
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')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.a... | code |
2007984/cell_3 | [
"application_vnd.jupyter.stderr_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')
print(test.shape)
test.head() | code |
2007984/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.utils.np_utils import to_categorical
import matplotlib.pyplot as plt
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')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astyp... | code |
2007984/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear a... | code |
2007984/cell_31 | [
"text_plain_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear a... | code |
2007984/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
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')
x_train = train[:, 1:].... | code |
2007984/cell_10 | [
"text_html_output_1.png",
"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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')... | code |
2007984/cell_12 | [
"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')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
x_train = x_trai... | code |
2007984/cell_5 | [
"application_vnd.jupyter.stderr_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')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape | code |
88091391/cell_13 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_9 | [
"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/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
def check_df(dataframe, head=5):
pass
check_df(df) | code |
88091391/cell_4 | [
"image_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88091391/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
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/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
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/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_20 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_6 | [
"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/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T | code |
88091391/cell_26 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_19 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
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/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_28 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_16 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
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/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_24 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_22 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
df.shape
df = df.drop(['car_ID'],... | code |
88091391/cell_10 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.describe().T
outliers = ['price']
plt.rcParams['figure.figsize'] = [8, 8]
sns.boxplot(data=df[outliers], ori... | code |
88091391/cell_5 | [
"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/car-price-prediction/CarPrice_Assignment.csv')
df.head() | code |
18115740/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from keras.optimizers import *
import keras
from keras.layers import *
from keras.models import *
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import * | code |
18115740/cell_8 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df['Test'] = False
test_df = pd.read_csv('../input/test.csv')
test_df['Test'] = True
df = pd.concat([train... | code |
18115740/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df['Test'] = False
test_df = pd.read_csv('../input/test.csv')
test_df['Test'] = True
df = pd.concat([train_df, test_df], sort=False)
corr = abs(train_df.corr())
price_corr = corr['SalePrice'].sort... | code |
2026584/cell_4 | [
"image_output_1.png"
] | from subprocess import check_output
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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
data = pd.read_csv('../input/Healt... | code |
2026584/cell_6 | [
"image_output_1.png"
] | from subprocess import check_output
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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
data = pd.read_csv('../input/Healt... | code |
2026584/cell_2 | [
"image_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
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
data = pd.read_csv('../input/Health_AnimalBites.c... | code |
2026584/cell_8 | [
"image_output_1.png"
] | from subprocess import check_output
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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
data = pd.read_csv('../input/Healt... | code |
2026584/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
data = pd.read_csv('../input/Healt... | code |
2026584/cell_12 | [
"image_output_1.png"
] | from subprocess import check_output
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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
data = pd.read_csv('../input/Healt... | code |
122260010/cell_13 | [
"text_plain_output_1.png"
] | import numpy
import numpy
import numpy
sampleArray = numpy.array([[3, 8, 9, 11], [15, 18, 21, 24], [27, 29, 33, 34], [39, 42, 45, 48], [51, 52, 57, 53]])
import numpy
sampleArray = numpy.array([[3, 8, 9, 11], [15, 18, 21, 24], [27, 29, 33, 34], [39, 42, 45, 48], [51, 52, 57, 53]])
sampleArray[0:5:2, 1:4:2] | code |
122260010/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
np.random.seed(100)
arr = np.random.randint(1, 11, size=(6, 10))
arr
r, c = np.shape(arr)
r
arr = np.ones((10, 10))
arr[0:-1:2, 0:-1:2] = 0
arr | code |
122260010/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
np.random.seed(100)
arr = np.random.randint(1, 11, size=(6, 10))
arr
r, c = np.shape(arr)
r | code |
122260010/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
np.random.seed(100)
arr = np.random.randint(1, 11, size=(6, 10))
arr
r, c = np.shape(arr)
r
for i in range(r):
print(np.unique(arr[i])) | code |
122260010/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
np.random.seed(100)
arr = np.random.randint(1, 11, size=(6, 10))
arr
r, c = np.shape(arr)
r
arr = np.ones((10, 10))
arr[0:-1:2, 0:-1:2] = 0
arr
Input: np.array([1, 2, 9, 1, 3, 7, 1, 2, 10])
arr2 = np.array([1, 2, 9, 1, 3, 7, 1, 2, 10])
for i in range(len(arr2)):
try:
... | code |
122260010/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
np.random.seed(100)
arr = np.random.randint(1, 11, size=(6, 10))
arr | code |
122260010/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
np.random.seed(100)
arr = np.random.randint(1, 11, size=(6, 10))
arr
r, c = np.shape(arr)
r
for i in range(r):
print(np.unique(arr[i])) | code |
73079382/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/input/english-converted-datasets/i... | code |
73079382/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/input/english-converted-datasets/i... | code |
73079382/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib as plot
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73079382/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/input/english-converted-datasets/i... | code |
73079382/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/input/english-converted-datasets/i... | code |
73079382/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/input/english-converted-datasets/i... | code |
73079382/cell_16 | [
"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)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/i... | code |
73079382/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/input/english-converted-datasets/i... | code |
73079382/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/input/english-converted-datasets/i... | code |
73079382/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_item_en = pd.read_csv('/kaggle/input/english-converted-datasets/items.csv')
df_submission_en = pd.read_csv('/kaggle/input/english-converted-datasets/sample_submission.csv')
df_item_cat_en = pd.read_csv('/kaggle/input/english-converted-datasets/i... | code |
90156742/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachin... | code |
90156742/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers'... | code |
90156742/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesw... | code |
90156742/cell_2 | [
"image_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesw... | code |
90156742/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90156742/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesw... | code |
90156742/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers'... | code |
90156742/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachin... | code |
90156742/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers'... | code |
90156742/cell_12 | [
"image_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesw... | code |
88092667/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 |
1004716/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
df1 = df.corr()
df1 = df1[df1 < 1]
sns.heatmap(df1, annot=True) | code |
1004716/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
xtest = X.ix[:100,]
ytest = Y.ix[:100,]
xtrain = X.ix[100:,]
ytrain = Y.ix[100:,]
K... | code |
1004716/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import ensemble
from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
xtest = X.ix[:100,]
ytest = Y.ix[:100,]
xtrain = X.ix[100:,]
ytrain = Y.ix[100:,]
RMClassifier = ensemble... | code |
1004716/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
df1 = df.corr()
df1 = df1[df1 < 1]
df.groupby(by='Type').mean()
sns.countplot(data=df, x='Type') | code |
1004716/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
xtest = X.ix[:100,]
ytest = Y.ix[:100,]
xtrain = X.ix[100:,]
ytrain = Y.ix[100:,]
RMClassifier = ensemble... | code |
1004716/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
xtest = X.ix[:100,]
ytest = Y.ix[:100,]
xtrain = X.ix[100:,]
ytrain = Y.ix[100:,]
from sklearn import tree
cl... | code |
1004716/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
xtest = X.ix[:100,]
ytest = Y.ix[:100,]
xtrain = X.ix[100:,]
ytrain = Y.ix[100:,]
from sklearn import tree
cl... | code |
1004716/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
print(df['Type'].value_counts().sort_values(ascending=False))
print() | code |
1004716/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/glass.csv')
df.head() | code |
1004716/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
xtest = X.ix[:100,]
ytest = Y.ix[:100,]
xtrain = X.ix[100:,]
ytrain = Y.ix[100:,]
K... | code |
1004716/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
df1 = df.corr()
df1 = df1[df1 < 1]
df.groupby(by='Type').mean() | code |
1004716/cell_10 | [
"text_html_output_1.png"
] | from sklearn import ensemble
from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('../input/glass.csv')
from sklearn.utils import shuffle
df = shuffle(df)
X = df.ix[:, :-1]
Y = df.ix[:, -1]
xtest = X.ix[:100,]
ytest = Y.ix[:100,]
xtrain = X.ix[100:,]
ytrain = Y.ix[100:,]
RMClassifier = ensemble... | code |
16150848/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import os
spotify_data = pd.read_csv('../input/data.csv')
print(spotify_data.columns)
print(spotify_data.shape) | code |
16150848/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import os
spotify_data = pd.read_csv('../input/data.csv')
print(max(spotify_data['Streams']))
print(np.where(spotify_data['Streams'] == max(spotify_data['Streams'])))
print(spotify_data['Track Name'][3145443]) | code |
16150848/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
spotify_data = pd.read_csv('../input/data.csv') | code |
16150848/cell_3 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import os
spotify_data = pd.read_csv('../input/data.csv')
spotify_data.head() | code |
74058977/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda,Flatten
from keras.models import Model,Sequential
from keras.optimizers import Adam
from keras.models import Model... | code |
74058977/cell_2 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
SIZE = 256
X_test = '../input/test-images'
Y_test = '../input/test-labels'
X_train = '../input/train-images'
Y_train = '../input/train-labels'
X_val = '../input/validat... | code |
74058977/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
SIZE = 256
X_test = '../input/test-images'
Y_test = '../input/test-labels'
X_train = '../input/train-images'
Y_train = '../input/train-... | code |
33101435/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
nRowsRead = None
df1 = pd.read_csv('/kaggle/input/tennis-20112019/atp.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'atp.csv'
nRow, nCol = df1.shape
print(f'There are {nRow} rows and {nCol} columns') | code |
33101435/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
nRowsRead = None
df1 = pd.read_csv('/kaggle/input/tennis-20112019/atp.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'atp.csv'
nRow, nCol = df1.shape
plotPerColumnDistribution(df1, 10, 5) | code |
33101435/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33101435/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
nRowsRead = None
df1 = pd.read_csv('/kaggle/input/tennis-20112019/atp.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'atp.csv'
nRow, nCol = df1.shape
df1.head(5) | code |
90130223/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/cell_26 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
90130223/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/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 |
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