path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18157731/cell_20 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_19 | [
"image_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18157731/cell_18 | [
"image_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_8 | [
"image_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes | code |
18157731/cell_15 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_12 | [
"image_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_5 | [
"image_output_1.png"
] | path = Path('../input/dataset')
path.ls() | code |
74056828/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/california-housing-prices/housing.csv')
df1 = pd.DataFrame()
for location in df['ocean_proximity'].unique():
df1 = df1.append(df.loc[df['ocean_proximity'] == location][:4])
df1 = df1.reset_index()
df = df1.drop(columns='index')
df
for i, c in zip(... | code |
74056828/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/california-housing-prices/housing.csv')
df1 = pd.DataFrame()
for location in df['ocean_proximity'].unique():
df1 = df1.append(df.loc[df['ocean_proximity'] == location][:4])
df1 = df1.reset_index()
df = df1.drop(columns='index')
df
for i, c in zip(... | code |
74056828/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/california-housing-prices/housing.csv')
df1 = pd.DataFrame()
for location in df['ocean_proximity'].unique():
df1 = df1.append(df.loc[df['ocean_proximity'] == location][:4])
df1 = df1.reset_index()
df = df1.drop(columns='index')
df
for i, c in zip(... | code |
74056828/cell_16 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/california-housing-prices/housing.csv')
df1 = pd.DataFrame()
for location in df['ocean_proximity'].unique():
df1 = df1.append(df.loc[df['ocean_proximity'] == location][:4])
df1 = df1.reset_index()
df = df1.drop(columns='index')
df
for i, c in zip(... | code |
74056828/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/california-housing-prices/housing.csv')
df1 = pd.DataFrame()
for location in df['ocean_proximity'].unique():
df1 = df1.append(df.loc[df['ocean_proximity'] == location][:4])
df1 = df1.reset_index()
df = df1.drop(columns='index')
df | code |
105200540/cell_25 | [
"image_output_1.png"
] | from tensorflow.keras.utils import to_categorical
import h5py
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
INPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/inputs.hdf5'
OUTPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/outputs.hdf5'
MODEL_PATH = 'output/srcnn.model'
P... | code |
105200540/cell_34 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import to_categorical
import cv2
import h5py
import numpy as np
import numpy as np
import numpy as np
INPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/inputs.hdf5'
OUTPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/outputs.hdf5'
MODEL_PATH = 'output/srcnn.model'
PL... | code |
105200540/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras import backend
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import plot_model
INPUTS_DB = '../input/ukbench100-patches-for-sr-h... | code |
105200540/cell_40 | [
"text_plain_output_1.png"
] | from PIL import Image
from tensorflow.keras.utils import to_categorical
import PIL
import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
INPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/inputs.hdf5'
OUTPUTS_DB = '../input/ukbench100-patches-f... | code |
105200540/cell_41 | [
"image_output_1.png"
] | from tensorflow.keras.utils import to_categorical
from typing import List
import cv2
import h5py
import matplotlib.image as img
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
INPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/i... | code |
105200540/cell_32 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import load_model
INPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/inputs.hdf5'
OUTPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/outputs.hdf5'
MODEL_PATH = 'output/srcnn.model'
PLOT_PATH = 'output/plot.png'
BATCH_SIZE = 128
NUM_EPOCHS = 10
SCALE = 2.0
INPUT_DIM = 33
... | code |
105200540/cell_38 | [
"text_plain_output_1.png"
] | for y in range(0, h - INPUT_DIM + 1, LABEL_SIZE):
for x in range(0, w - INPUT_DIM + 1, LABEL_SIZE):
crop = scaled[y:y + INPUT_DIM, x:x + INPUT_DIM].astype('float32')
P = model.predict(np.expand_dims(crop, axis=0))
P = P.reshape((LABEL_SIZE, LABEL_SIZE, 3))
output[y + PAD:y + PAD + LA... | code |
105200540/cell_35 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from tensorflow.keras.utils import to_categorical
import PIL
import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
INPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/inputs.hdf5'
OUTPUTS_DB = '../input/ukbench100-patches-f... | code |
105200540/cell_24 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras import backend
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import plot_model
INPUTS_DB = '../input/ukbench100-patches-for-sr-h... | code |
105200540/cell_22 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | H = model.fit_generator(super_res_generator(inputs.generator(), targets.generator()), steps_per_epoch=inputs.num_images // BATCH_SIZE, epochs=NUM_EPOCHS, verbose=1) | code |
105200540/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from tensorflow.keras import backend
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import plot_model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from ... | code |
105200540/cell_36 | [
"text_plain_output_1.png"
] | from PIL import Image
from tensorflow.keras.utils import to_categorical
import PIL
import cv2
import h5py
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
INPUTS_DB = '../input/ukbench100-patches-for-sr-hdf5/output/inputs.hdf5'
OUTPUTS_DB = '../input/ukbench100-patches-f... | code |
74070177/cell_2 | [
"text_plain_output_1.png"
] | !apt install libasound2-dev portaudio19-dev libportaudio2 libportaudiocpp0 ffmpeg -y libasound2-dev | code |
74070177/cell_1 | [
"text_plain_output_1.png"
] | !git clone https://github.com/mcdermottLab/pycochleagram.git | code |
74070177/cell_7 | [
"text_plain_output_1.png"
] | from distutils.dir_util import copy_tree
from distutils.dir_util import copy_tree
copy_tree('../input/cochleagramfile', '../working/') | code |
74070177/cell_3 | [
"text_plain_output_1.png"
] | !pip install pqdm | code |
74070177/cell_22 | [
"text_plain_output_1.png"
] | from pqdm.processes import pqdm
from scipy import signal
import cochleagram as cgram
import cv2
import erbfilter as erb
import glob
import librosa
import librosa
import librosa
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas a... | code |
74070177/cell_12 | [
"text_plain_output_1.png"
] | import glob
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
os.chdir('/kaggle/working')
output_dir = './data'
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
os.mkdir('./data/nocall')
path_to_json = '../input/freefield1... | code |
74070177/cell_5 | [
"text_html_output_1.png"
] | print(os.getcwd())
os.chdir('./pycochleagram')
print(os.getcwd())
!python setup.py install | code |
128016087/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128016087/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128016087/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128016087/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128016087/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
exercise = pd.read_csv('/kaggle/input/fmendesdat263xdemos/exercise.csv')
calories = pd.read_csv('/kaggle/input/fmendesdat263xdemos/calories.csv')
exercise['Calories_Burned'] = calories['Calories']
exercise = exercise.drop(['User_ID'], axis=1)
exercise | code |
128016087/cell_2 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import warnings
import pandas as pd
import numpy as np
import random
import os
import gc
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge
import matplotlib.py... | code |
128016087/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2, 3, figsize=(10, 10))
sns.boxplot(y=train['Age'], ax=axes[0][0])
sns.boxplot(y=train['Height'], ax=axes[0][1])
sns.boxplot(y=train['Weight'], ax=axes[0][2])
sns.boxplot(y=train['Duration'], ax=axes[1][0])
sns.boxplot(y=train['Heart_Rate'... | code |
128016087/cell_19 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128016087/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128016087/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128016087/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128016087/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
90120014/cell_4 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
path = '../input/ham1000-segmentation-and-classification/images/ISIC_0024306.jpg'
img = cv2.imread(path)
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = (img2[:, :, 0], img2[:, :, 1], img2[:, :, 2])
hist_h = cv2.calcHist([h], [0], None, [256], [0, 256])
hist_h = cv2.no... | code |
90120014/cell_14 | [
"image_output_1.png"
] | import cv2
import glob
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)
df_labels = pd.read_csv('../input/ham1000-segmentation-and-classification/GroundTruth.csv')
df_labels['image'] = df_labels['image'] + '.jpg'
labels = ['M... | code |
90120014/cell_10 | [
"image_output_1.png"
] | import cv2
import glob
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)
df_labels = pd.read_csv('../input/ham1000-segmentation-and-classification/GroundTruth.csv')
df_labels['image'] = df_labels['image'] + '.jpg'
labels = ['M... | code |
130005921/cell_9 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | def data_exploration(df):
return
data_exploration(greeks)
data_exploration(train) | code |
130005921/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | def data_exploration(df):
print(f'Shape of this set:', df.shape)
print('*' * 90)
print(f'Columns of this set:', df.columns)
print('*' * 90)
print(f'Missing rows in this set:', df.info())
print('*' * 90)
return df.head()
data_exploration(greeks) | code |
130005921/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | def data_exploration(df):
return
data_exploration(greeks)
data_exploration(sample_submission) | code |
130005921/cell_8 | [
"text_plain_output_1.png"
] | def data_exploration(df):
return
data_exploration(greeks)
data_exploration(test) | code |
130005921/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import seaborn as sns
def data_exploration(df):
return
data_exploration(greeks)
def impute_and_replace(df):
numerical_columns = df.select_dtypes(include=[np.number]).columns
df[numerical_columns] = df[numerical_columns].fillna(df[numerical_columns].mean())
categorical_column = 'EJ'... | code |
130005921/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import seaborn as sns
sns.heatmap(train.isnull(), cmap='cool') | code |
130005921/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import seaborn as sns
def data_exploration(df):
return
data_exploration(greeks)
def impute_and_replace(df):
numerical_columns = df.select_dtypes(include=[np.number]).columns
df[numerical_columns] = df[numerical_columns].fillna(df[numerical_columns].mean())
categorical_column = 'EJ'... | code |
2041217/cell_13 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'Co... | code |
2041217/cell_9 | [
"image_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'CommuteTime', 'HasChildren', 'AttendedBootcamp', 'HasDebt', 'HoursLearning... | code |
2041217/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'CommuteTime', 'HasChildren', 'AttendedBootcamp', 'HasDebt', 'HoursLearning', 'MonthsProgramming', 'Income'])
df.rename(columns={'Gender': 'IsWoman'}, inplace=True)
df['IsWoman'] = df['IsWoman'] == 'female'
... | code |
2041217/cell_6 | [
"text_html_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
import pandas as pd
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'CommuteTime', 'HasChildren', 'AttendedBootcamp', 'HasDebt', 'HoursLearning', 'MonthsProgramming', 'Income'])
df.rename(columns={'Gender': 'IsWoman'}, inplace=T... | code |
2041217/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'Co... | code |
2041217/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 statsmodels.api as sm
from statsmodels.graphics.gofplots import ProbPlot
from sklearn.linear_model import ElasticNetCV | code |
2041217/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
import pandas as pd
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'CommuteTime', 'HasChildren', 'AttendedBootcamp', 'HasDebt', 'HoursLearning', 'MonthsProgramming', 'Income'])
df.rename(columns={'Gender': 'IsWoman'}, inplace=T... | code |
2041217/cell_15 | [
"image_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'Co... | code |
2041217/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'CommuteTime', 'HasChildren', 'AttendedBootcamp', 'HasDebt', 'HoursLearning', 'MonthsProgramming', 'Income'])
df.rename(columns={'Gender': 'IsWoman'}, inplace=True)
df['IsWoman'] = df['IsWoman'] == 'female'
... | code |
2041217/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'CommuteTime', 'HasChildren', 'AttendedBootcamp', 'HasDebt', 'HoursLearning', 'MonthsProgramming', 'Income'])
df.rename(columns={'... | code |
2041217/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'CommuteTime', 'HasChil... | code |
2041217/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'Co... | code |
2041217/cell_5 | [
"image_output_1.png"
] | from sklearn.linear_model import ElasticNetCV
import pandas as pd
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', usecols=['Gender', 'Age', 'CommuteTime', 'HasChildren', 'AttendedBootcamp', 'HasDebt', 'HoursLearning', 'MonthsProgramming', 'Income'])
df.rename(columns={'Gender': 'IsWoman'}, inplace=T... | code |
34136171/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
neg_age_idx = cleaned_data[cleaned_data['A... | code |
34136171/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum() | code |
34136171/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes | code |
34136171/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
neg_age_idx = cleaned_data[cleaned_data['A... | code |
34136171/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
cleaned_data.head(2) | code |
34136171/cell_6 | [
"application_vnd.jupyter.stderr_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 |
34136171/cell_54 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
neg_age_idx = cleaned_data[cleaned_data['A... | code |
34136171/cell_60 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
neg_age_i... | code |
34136171/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df['Neighbourhood'].unique() | code |
34136171/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
neg_age_idx = cleaned_data[cleaned_data['A... | code |
34136171/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
neg_age_idx = cleaned_data[cleaned_data['A... | code |
34136171/cell_7 | [
"image_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.head(2) | code |
34136171/cell_62 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
base_color = sns.color_palette()[0]
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.... | code |
34136171/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df['Age'].describe() | code |
34136171/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
neg_age_idx = cleaned_data[cleaned_data['A... | code |
34136171/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum()
data_df.dtypes
data_df.duplicated().sum()
cleaned_data = data_df.copy()
cleaned_data.rename(columns={'Hipertension': 'Hypertension'}, inplace=True)
neg_age_idx = cleaned_data[cleaned_data['A... | code |
34136171/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('/kaggle/input/noshowappointments/KaggleV2-May-2016.csv')
data_df.isnull().sum() | code |
72080001/cell_9 | [
"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_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
df_train
df_train.describe() | code |
72080001/cell_6 | [
"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_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
print(submission.shape)
submission | code |
72080001/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 |
72080001/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_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
print(df_train.shape)
df_train | code |
72080001/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
df_train
df_train.info() | code |
72080001/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
d... | code |
72080001/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
s... | code |
72080001/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from lightgbm import LGBMRegressor | code |
32063423/cell_21 | [
"text_plain_output_1.png"
] | from xgboost import XGBRegressor
model2 = XGBRegressor(n_estimators=1000)
model2.fit(X_train, y_train[:, 1]) | code |
32063423/cell_13 | [
"text_plain_output_1.png"
] | from fastai.tabular import add_datepart
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_train['Date'] = pd.to_datetime(df_trai... | code |
32063423/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_date_min = df_train['Date'].min()
train_date_max = df_train['Date'].max()
print('Minimum date from training set: {}'.format(tr... | code |
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