path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128001996/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
target = 'yield'
df1 = pd.read_csv(TRAIN_CSV)
df1.rename({'Id': 'id'}, axis=1, inplace=True)
df1['test'] = 0
df1['gen'] = 1
df2 = pd.read_csv(TEST_CSV)
df2.rename({'Id': 'id'}, axis=1, inplace=True)
df2['test'] = 1
df2['gen'] = 1
df3 = pd.read... | code |
105196211/cell_21 | [
"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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/cell_13 | [
"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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/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)
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum() | code |
105196211/cell_25 | [
"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 pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('dark_background')
df = pd.read_csv('../input/engineering-graduate-salary-prediction/E... | code |
105196211/cell_4 | [
"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)
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df | code |
105196211/cell_34 | [
"image_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 pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('dark_background')
df = pd.read_csv('../input/engineering-gradu... | code |
105196211/cell_23 | [
"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 seaborn as sns
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', ... | code |
105196211/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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/cell_26 | [
"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 pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('dark_background')
df = pd.read_csv('../input/engineering-graduate-salary-prediction/E... | code |
105196211/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)
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape | code |
105196211/cell_19 | [
"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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/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 |
105196211/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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.head(5) | code |
105196211/cell_18 | [
"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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/cell_32 | [
"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 pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('dark_background')
df = pd.read_csv('../input/engineering-gradu... | code |
105196211/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)
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns | code |
105196211/cell_15 | [
"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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/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)
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/cell_38 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
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 pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('dark_b... | code |
105196211/cell_17 | [
"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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/cell_35 | [
"image_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 pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('dark_background')
df = pd.read_csv('../input/engineering-gradu... | code |
105196211/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
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
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df... | code |
105196211/cell_14 | [
"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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', '12graduation', '12boar... | code |
105196211/cell_22 | [
"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 seaborn as sns
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns
df2 = df.drop(columns=['ID', 'DOB', '10board', ... | code |
105196211/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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.describe() | code |
105196211/cell_37 | [
"text_plain_output_1.png"
] | import numpy as np
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
df = pd.read_csv('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df... | code |
105196211/cell_12 | [
"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('../input/engineering-graduate-salary-prediction/Engineering_graduate_salary.csv')
df
df.columns
df.isnull().sum()
df.shape
df.columns | code |
90104932/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Countr... | code |
90104932/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
print('Preview of the data: ')
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.head(5) | code |
90104932/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Countr... | code |
90104932/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
print('Description of the data: ')
hlc_data.describe() | code |
90104932/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Country)'].str.replace('%', '', regex=False)
cleaned_hlc_data[... | code |
90104932/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Countr... | code |
90104932/cell_1 | [
"text_plain_output_1.png"
] | import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
pd.plotting.register_matplotlib_converters()
print('Setup Complete') | code |
90104932/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Country)'].str.replace('%', '', regex=False)
cleaned_hlc_data[... | code |
90104932/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Countr... | code |
90104932/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Countr... | code |
90104932/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Country)'].str.replace('%', '', regex=False)
cleaned_hlc_data[... | code |
90104932/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
hlc_data.columns
cleaned_hlc_data = hlc_data.copy()
cleaned_hlc_data['Obesity levels(Country)'] = cleaned_hlc_data['Obesity levels(Country)'].str.replace('%', '', regex=False)
cleaned_hlc_data[... | code |
90104932/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hlc_data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
print('City and Rank + The 10 metrics: ')
hlc_data.columns | code |
34129954/cell_4 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
TRAINING_DIR = '/kaggle/input/tomato/New Plant Diseases Dataset(Augmented)/train/'
VALIDATION_DIR = '/kaggle/input/tomato/New Plant Diseases Dataset(Augmented)/valid/'
train_gen = ImageDataGenerator(rescale=1.0 / 255, rotation_range=0, width_shift_ran... | code |
34129954/cell_6 | [
"text_plain_output_1.png"
] | from keras.layers import Convolution2D,MaxPooling2D,Flatten,Dense,Dropout
from keras.models import Sequential
model = Sequential()
model.add(Convolution2D(96, 11, strides=(4, 4), padding='valid', input_shape=(227, 227, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid'))
... | code |
34129954/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
tf.test.gpu_device_name()
import tensorflow as tf
import keras_preprocessing
from tensorflow.keras.preprocessing import image
import pickle
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import TensorBoa... | code |
34129954/cell_1 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
tf.test.gpu_device_name() | code |
34129954/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Convolution2D,MaxPooling2D,Flatten,Dense,Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import keras
TRAINING_DIR = '/kaggle/input/tomato/New Plant Diseases Dataset(Augmented)/train/'
VALIDAT... | code |
34129954/cell_3 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
TRAINING_DIR = '/kaggle/input/tomato/New Plant Diseases Dataset(Augmented)/train/'
VALIDATION_DIR = '/kaggle/input/tomato/New Plant Diseases Dataset(Augmented)/valid/'
train_gen = ImageDataGenerator(rescale=1.0 / 255, rotation_range=0, width_shift_ran... | code |
16150103/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import pickle
import pickle
with open('../input/X.pickle', 'rb') as fp:
X_feature = pickle.load(fp)
with open('../input/Y.pickle', 'rb')... | code |
16150103/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorfl... | code |
16150103/cell_8 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import pickle
import pickle
with open('../input/X.pickle', 'rb') as fp:
X_feature = pickle.load(fp)
with open('../input/Y.pickle', 'rb')... | code |
16120502/cell_42 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_cs... | code |
16120502/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
test.head() | code |
16120502/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
train.drop(['Id'], axis=1, inplace=True)
test.drop(['Id']... | code |
16120502/cell_34 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
t... | code |
16120502/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
train.drop(['Id'], axis=1, inplace=True)
test.drop(['Id'], axis=1, inplace=True)
train.iloc[0:5, :3]
null_cols ... | code |
16120502/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
train.drop(['Id'], axis=1, inplace=True)
test.drop(['Id'], axis=1, inplace=True)
train.iloc[... | code |
16120502/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv') | code |
16120502/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
train.drop(['Id'], axis=1, inplace=True)
test.drop(['Id'], axis=1, inplace=True)
train.iloc[0:5, :3]
print('*' ... | code |
16120502/cell_18 | [
"text_plain_output_1.png"
] | """
Some functions to start off with:
train.sample()
train.describe()
train.describe(include=['O'])
train.describe(include='all')
train.head()
train.tail()
train.value_counts().sum()
train.isnull().sum()
train.count()
train.fillna()
train.fillna(train[col].mode(), ... | code |
16120502/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
t... | code |
16120502/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
train.drop(['Id'], axis=1, inplace=True)
test.drop(['Id'], axis=1, inplace=True)
train.iloc[0:5, :3] | code |
16120502/cell_35 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
train.drop(['Id'], axis=1, inplace=True)
test.drop(['Id'], axis=1, inplace=True)
train.iloc[... | code |
16120502/cell_10 | [
"image_output_1.png"
] | import pandas_profiling
import numpy as np
import random as rand
import datetime as dt
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import norm, skew
from scipy.special import boxcox1p
from scipy.stats import boxcox_normmax
from scipy import stats
from sklearn.metrics import mean_squared_error... | code |
16120502/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('09-house-train.csv')
test = pd.read_csv('09-house-test.csv')
train.head() | code |
105189792/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/attackincident/1.csv', nrows=3000, skiprows=range(1, 74732))
pd.options.display.max_info_columns = 200
data.drop(index=data[(data['iday'] < 1) | (data['iday'] > 31) | (data['imonth'] < 1) | (data['imonth'] > 12) | (data['iyear'] < 1998) | (data['iyear'] > 2017)].index... | code |
105189792/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/attackincident/1.csv', nrows=3000, skiprows=range(1, 74732))
pd.options.display.max_info_columns = 200
data.info() | code |
105189792/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/attackincident/1.csv', nrows=3000, skiprows=range(1, 74732))
pd.options.display.max_info_columns = 200
data.drop(index=data[(data['iday'] < 1) | (data['iday'] > 31) | (data['imonth'] < 1) | (data['imonth'] > 12) | (data['iyear'] < 1998) | (data['iyear'] > 2017)].index... | code |
105189792/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/attackincident/1.csv', nrows=3000, skiprows=range(1, 74732))
pd.options.display.max_info_columns = 200
data.drop(index=data[(data['iday'] < 1) | (data['iday'] > 31) | (data['imonth'] < 1) | (data['imonth'] > 12) | (data['iyear'] < 1998) | (data['iyear'] > 2017)].index... | code |
105189792/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/attackincident/1.csv', nrows=3000, skiprows=range(1, 74732))
pd.options.display.max_info_columns = 200
data.drop(index=data[(data['iday'] < 1) | (data['iday'] > 31) | (data['imonth'] < 1) | (data['imonth'] > 12) | (data['iyear'] < 1998) | (data['iyear'] > 2017)].index... | code |
105189792/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/attackincident/1.csv', nrows=3000, skiprows=range(1, 74732))
print('数据大小:', data.shape)
data.head(1) | code |
105189792/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/attackincident/1.csv', nrows=3000, skiprows=range(1, 74732))
pd.options.display.max_info_columns = 200
data.drop(index=data[(data['iday'] < 1) | (data['iday'] > 31) | (data['imonth'] < 1) | (data['imonth'] > 12) ... | code |
2016018/cell_2 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation
from sklearn.svm import SVR
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
train = pd.read_table('../input/train.tsv')
drpNa = train.drop(['train_id', 'name', 'category_name', 'brand_name', 'item_description'], 1)
drpNa = drpNa.dropna()
def rmsle(h... | code |
2016018/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn import cross_validation
from sklearn.svm import SVR
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
train = pd.read_table('../input/train.tsv')
drpNa = train.drop(['train_id', 'name', 'category_name', 'brand_name', 'item_description'], 1)
drpNa = drpNa.dropna()
def rmsle(h... | code |
128027453/cell_13 | [
"text_html_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.rep... | code |
128027453/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', 'current', 'not current', 'ever'], [0.5, 0, 0.5, 1, 0... | code |
128027453/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.head() | code |
128027453/cell_20 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, recall_score
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=15, n_jobs=-1)
rnd_clf.fit(X_train, y_train)
rnd_clf_pre... | code |
128027453/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', 'current', 'not current', 'ever'], [0.5, 0, 0.5, 1, 0... | code |
128027453/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', ... | code |
128027453/cell_19 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, recall_score
from sklearn.ensemble import RandomForestClassifier
rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=15, n_jobs=-1)
rnd_clf.fit(X_train, y_train)
rnd_clf_preds = rnd_clf.predict(X_test)
print(... | code |
128027453/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128027453/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', 'current', 'not current', 'ever'], [0.5, 0, 0.5, 1, 0... | code |
128027453/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, recall_score
from sklearn.svm import SVC
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, recall_score
svm_clf = SVC()
svm_clf.fit(X_train, y_train)
svm_clf_preds = svm_clf.predict(X_test)
print('SVM Classifier accuracy on validation data : ', recall_... | code |
128027453/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.replace(['No Info', 'never', 'former', 'current', 'not current', 'ever'], [0.5, 0, 0.5, 1, 0... | code |
128027453/cell_16 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female... | code |
128027453/cell_14 | [
"text_html_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df = df[df.gender != 'Other']
df['gender'].replace(['Female', 'Male'], [0, 1], inplace=True)
df.smoking_history.rep... | code |
104115434/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')
df_subset = df[['patient_id']][:500]
pred = torch.tensor(np.linspace(0, 1, 500))
target = torch.ones(500, dtype=torch.float64)
df_subset['CE'], df_subset['LAA'] = [pred.nu... | code |
104115434/cell_9 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import torch
df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')
df_subset = df[['patient_id']][:500]
pred = torch.tensor(np.linspace(0, 1, 500))
target = torch.ones(500, dtype=torch.float64)
df_subset['CE'], df_subset['LAA'] = [pred.numpy(), 1 - pred.numpy()]
df_subse... | code |
104115434/cell_6 | [
"image_output_1.png"
] | from itables import init_notebook_mode
import seaborn as sns
from itables import init_notebook_mode
init_notebook_mode(all_interactive=True)
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from sklearn.metrics import hinge_loss
i... | code |
104115434/cell_5 | [
"image_output_1.png"
] | !pip install itables | code |
130023373/cell_9 | [
"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"
] | import pandas as pd #dataframe manipulation
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/test.csv')
train.info() | code |
130023373/cell_4 | [
"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"
] | !pip install distance -q | code |
130023373/cell_34 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw, ImageEnhance #for read the image
from tqdm import tqdm_notebook
import cv2 #for read the image
import os
import pandas as pd #dataframe manipulation
import seaborn as sns #for visualization
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.r... | code |
130023373/cell_23 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw, ImageEnhance #for read the image
import os
import pandas as pd #dataframe manipulation
import seaborn as sns #for visualization
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/test.csv'... | code |
130023373/cell_20 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw, ImageEnhance #for read the image
import os
import pandas as pd #dataframe manipulation
import seaborn as sns #for visualization
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/test.csv'... | code |
130023373/cell_26 | [
"image_output_1.png"
] | from PIL import Image, ImageDraw, ImageEnhance #for read the image
from tqdm import tqdm_notebook
import cv2 #for read the image
import os
import pandas as pd #dataframe manipulation
import seaborn as sns #for visualization
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.r... | code |
130023373/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd #dataframe manipulation
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/test.csv')
test.info() | code |
130023373/cell_32 | [
"text_html_output_1.png"
] | from PIL import Image, ImageDraw, ImageEnhance #for read the image
from tqdm import tqdm_notebook
import cv2 #for read the image
import os
import pandas as pd #dataframe manipulation
import seaborn as sns #for visualization
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.r... | code |
130023373/cell_28 | [
"image_output_1.png"
] | from PIL import Image, ImageDraw, ImageEnhance #for read the image
from tqdm import tqdm_notebook
import cv2 #for read the image
import os
import pandas as pd #dataframe manipulation
import seaborn as sns #for visualization
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.r... | code |
130023373/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd #dataframe manipulation
import seaborn as sns #for visualization
train = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/train.csv')
test = pd.read_csv('/kaggle/input/machinehack-watermark-challenge/test.csv')
_ = sns.countplot(x=train['Label'], order=train['Label'].value_counts().inde... | code |
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