path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73072460/cell_56 | [
"text_html_output_1.png"
] | import tensorflow as tf
training_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0, rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, horizontal_flip=True, vertical_flip=True)
training_generator = training_data_gen.flow_from_dataframe(datafram... | code |
73072460/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
import pathlib
data_path = pathlib.Path('../input/a-large-scale-fish-dataset/Fish_Dataset/Fish_Dataset')
all_images = list(data_path.glob('*/*/*.jpg')) + list(data_path.glob('*/*/*.png'))
images = []
labels = []
for item in all_images:
path = os.path.normpath(item)
splits = pat... | code |
73072460/cell_40 | [
"text_html_output_1.png"
] | from IPython.display import Image
Image(url='https://miro.medium.com/max/658/0*jLoqqFsO-52KHTn9.gif', width=750, height=500) | code |
73072460/cell_11 | [
"text_html_output_1.png"
] | import tensorflow as tf
training_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0, rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, horizontal_flip=True, vertical_flip=True)
training_generator = training_data_gen.flow_from_dataframe(datafram... | code |
73072460/cell_60 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import tensorflow as tf
training_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0, rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, horizontal_flip=True, vertical_flip=True)
training_generator = training_data_gen.flow_from_dataframe(datafram... | code |
73072460/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import pathlib
data_path = pathlib.Path('../input/a-large-scale-fish-dataset/Fish_Dataset/Fish_Dataset')
all_images = list(data_path.glob('*/*/*.jpg')) + list(data_path.glob('*/*/*.png'))
images = []
labels = []
for item in all_images:
path = os.pat... | code |
73072460/cell_59 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import pathlib
import tensorflow as tf
data_path = pathlib.Path('../input/a-large-scale-fish-dataset/Fish_Dataset/Fish_Dataset')
all_images = list(data_path.glob('*/*/*.jpg')) + list(data_path.glob('*/*/*.png'))
images = []
labels = []
for item in all_... | code |
73072460/cell_58 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import pathlib
import tensorflow as tf
data_path = pathlib.Path('../input/a-large-scale-fish-dataset/Fish_Dataset/Fish_Dataset')
all_images = list(data_path.glob('*/*/*.jpg')) + list(data_path.glob('*/*/*.png'))
images = []
labels = []
for item in all_... | code |
73072460/cell_16 | [
"image_output_1.png"
] | import tensorflow as tf
training_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0, rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, horizontal_flip=True, vertical_flip=True)
training_generator = training_data_gen.flow_from_dataframe(datafram... | code |
73072460/cell_47 | [
"text_html_output_1.png"
] | from IPython.display import Image
Image(url='https://nico-curti.github.io/NumPyNet/NumPyNet/images/maxpool.gif', width=750, height=500) | code |
73072460/cell_17 | [
"text_plain_output_1.png"
] | import tensorflow as tf
training_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0, rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, horizontal_flip=True, vertical_flip=True)
training_generator = training_data_gen.flow_from_dataframe(datafram... | code |
73072460/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import Image
Image(url='https://www.researchgate.net/profile/Lavender-Jiang-2/publication/343441194/figure/fig2/AS:921001202311168@1596595206463/Basic-CNN-architecture-and-kernel-A-typical-CNN-consists-of-several-component-types.ppm', width=750, height=500) | code |
73072460/cell_53 | [
"text_html_output_1.png"
] | import tensorflow as tf
training_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0, rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, horizontal_flip=True, vertical_flip=True)
training_generator = training_data_gen.flow_from_dataframe(datafram... | code |
73072460/cell_27 | [
"text_plain_output_1.png"
] | from IPython.display import Image
from IPython.display import Image
Image(url='https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRwed5zvnSDt0zrFd_gf-kUIMoF7Nm6FXIwDw&usqp=CAU', width=750, height=500) | code |
73072460/cell_37 | [
"text_html_output_1.png"
] | from IPython.display import Image
Image(url='https://i.stack.imgur.com/CQtHP.gif', width=750, height=500) | code |
74042725/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product_Name')
sales.columns
num_cols = sales._get_numeric_dat... | code |
74042725/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product_Name')
sales.columns | code |
74042725/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns | code |
74042725/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product... | code |
74042725/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product_Name')
sales.columns
num_cols =... | code |
74042725/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
len(sales['Order_ID'].unique()) | code |
74042725/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product... | code |
74042725/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product... | code |
74042725/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product_Name')
sales.columns
num_cols = sales._get_numeric_dat... | code |
74042725/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
len(sales['Product_Name'].unique()) | code |
74042725/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.head() | code |
74042725/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product_Name')
sales.columns
num_cols =... | code |
74042725/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape
sales_id = sales['Order_ID']
sales_pname = sales['Product_Name']
sales = sales.drop(columns='Order_ID')
sales = sales.drop(columns='Product_Name')
sales.columns
num_cols = sales._get_numeric_dat... | code |
74042725/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
sales = pd.read_csv('../input/sales-store-product-details/Salesstore.csv')
sales.columns
sales.shape | code |
104129189/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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020") | code |
104129189/cell_39 | [
"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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
sns.set_style('whitegrid')
serie_mean_model = df.groupby('model')['pric... | code |
104129189/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 |
104129189/cell_45 | [
"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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
sns.set_style('whitegrid')
serie_mean_model = df.groupby('model')['pric... | code |
104129189/cell_18 | [
"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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
def serie(data):
return df[data].value_counts().sort_values(ascending=True)
def Barplot(serie, title):
colors = ['#77dd7... | code |
104129189/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
df.info() | code |
104129189/cell_15 | [
"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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
def serie(data):
return df[data].value_counts().sort_values(ascending=True)
def Barplot(serie, title):
colors = ['#77dd7... | code |
104129189/cell_38 | [
"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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
sns.set_style('whitegrid')
serie_mean_model = df.groupby('model')['pric... | code |
104129189/cell_43 | [
"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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
sns.set_style('whitegrid')
serie_mean_model = df.groupby('model')['pric... | code |
104129189/cell_24 | [
"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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
sns.set_style('whitegrid')
serie_mean_model = df.groupby('model')['pric... | code |
104129189/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
def serie(data):
return df[data].value_counts().sort_values(ascending=True)
def Barplot(serie, title):
colors = ['#77dd7... | code |
104129189/cell_22 | [
"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/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
def serie(data):
return df[data].value_counts().sort_values(ascending=True)
def Barplot(serie, title):
colors = ['#77dd7... | code |
104129189/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/ford-car-price-prediction/ford.csv')
df.query("model == ' Mustang' and year == 2020")
def serie(data):
return df[data].value_counts().sort_values(ascending=True)
def Barplot(serie, title):
colors = ['#77dd7... | code |
106209352/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature... | code |
106209352/cell_9 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature = [feature for feature in dataset.columns if dataset... | code |
106209352/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na | code |
106209352/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature = [feature for feature in dataset.columns if dataset... | code |
106209352/cell_2 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
dataset.head() | code |
106209352/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature... | code |
106209352/cell_19 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().s... | code |
106209352/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature = [feature for feature in dataset.columns if dataset... | code |
106209352/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature = [feature for feat... | code |
106209352/cell_16 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature... | code |
106209352/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().s... | code |
106209352/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature... | code |
106209352/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature = [feature for feat... | code |
106209352/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum()
numerical_feature... | code |
106209352/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/the-spotify-hit-predictor-dataset/dataset-of-90s.csv')
features_with_na = [feature for feature in dataset.columns if dataset[feature].isnull().sum() > 1]
features_with_na
dataset.isnull().sum() | code |
122251856/cell_5 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_20.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png"... | from cv2 import resize
import glob
import matplotlib.pyplot as plt
import numpy as np
import os
import tqdm.auto as tqdm
olci_root = '/kaggle/input/medisar-olci'
available_olci = os.listdir(olci_root)
def plot_olci(key):
with open(f'{olci_root}/{key}/metadata.txt', 'r') as file:
lines = [line.replace(... | code |
89123667/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold, KFold, TimeSeriesSplit
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nrows = None
def train_eval_kfolds(X, y, n_splits=5, random_state=None):
assert hasattr(X, 'iloc')
assert hasattr(y, 'iloc')
skf = TimeSe... | code |
89123667/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)
nrows = None
train_df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', nrows=nrows)
target_mean = train_df['congestion'].mean()
target_mean
(train_df['congestion'] - target_mean).abs... | code |
89123667/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nrows = None
train_df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', nrows=nrows)
train_df['congestion'] | code |
89123667/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('../input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89123667/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nrows = None
train_df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', nrows=nrows)
train_df['congestion'].hist(bins=100) | code |
89123667/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nrows = None
train_df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', nrows=nrows)
target_mean = train_df['congestion'].mean()
target_mean | code |
89123667/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold, KFold, TimeSeriesSplit
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nrows = None
def train_eval_kfolds(X, y, n_splits=5, random_state=None):
assert hasattr(X, 'iloc')
assert hasattr(y, 'iloc')
skf = TimeSe... | code |
89123667/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import StratifiedKFold, KFold, TimeSeriesSplit
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nrows = None
def train_eval_kfolds(X, y, n_splits=5, random_state=None):
assert hasattr(X, 'iloc')
assert hasattr(y, 'iloc')
skf = TimeSe... | code |
89123667/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold, KFold, TimeSeriesSplit
from sklearn.metrics import accuracy_score
from scipy import stats
from sklearn.linear_model import Logisti... | code |
89123667/cell_14 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
print('overall scores', np.mean(scores)) | code |
89123667/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nrows = None
train_df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', nrows=nrows)
features = [c for c in train_df.columns if c not in ['congestion']]
X = train_df[features]
y = trai... | code |
89123667/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nrows = None
train_df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', nrows=nrows)
train_df.head() | code |
33102709/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
case_c = pd.read_csv('/kaggle/input/group-c-data/analysis_data_test.csv')
# reshape dataframe into pivot table
d = case_c.pivot(index = 'x_value', columns = 'n_value', values = 'avg_peak_bed_usage')
# set visualization features like title, axis na... | code |
33102709/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
case_c = pd.read_csv('/kaggle/input/group-c-data/analysis_data_test.csv')
# reshape dataframe into pivot table
d = case_c.pivot(index = 'x_value', columns = 'n_value', values = 'avg_peak_bed_usage')
# set visualization features like title, axis na... | code |
33102709/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
case_c = pd.read_csv('/kaggle/input/group-c-data/analysis_data_test.csv')
# reshape dataframe into pivot table
d = case_c.pivot(index = 'x_value', columns = 'n_value', values = 'avg_peak_bed_usage')
# set visualization features like title, axis na... | code |
33102709/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
case_c = pd.read_csv('/kaggle/input/group-c-data/analysis_data_test.csv')
d = case_c.pivot(index='x_value', columns='n_value', values='avg_peak_bed_usage')
d.columns.name = 'Initial Infections'
plot1 = d.plot(title='Policy Strictness v. Hospital B... | code |
33102709/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
case_c = pd.read_csv('/kaggle/input/group-c-data/analysis_data_test.csv')
# reshape dataframe into pivot table
d = case_c.pivot(index = 'x_value', columns = 'n_value', values = 'avg_peak_bed_usage')
# set visualization features like title, axis na... | code |
33102709/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
case_c = pd.read_csv('/kaggle/input/group-c-data/analysis_data_test.csv')
case_c.head(10) | code |
2002244/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
workdir = '../input/'
basins_data = pd.read_csv(workdir + 'BasinCharacteristics.csv')
features_cols = basins_data.columns[1:4]
target_col = basins_data.columns[0]
print('Feature column(s):\n{}\n'.format(features_cols))
print('... | code |
2002244/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
workdir = '../input/'
basins_data = pd.read_csv(workdir + 'BasinCharacteristics.csv')
features_cols = basins_data.columns[1:4]
target_col = basins_data.columns[0]
X = basins_data[features_co... | code |
2002244/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import plotly
import pandas as pd
import pylab
import matplotlib.pyplot as plt
import calendar
import seaborn
import math
from sklearn.svm import SVR
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from sklearn import preprocessin... | code |
2002244/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2002244/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
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)
workdir = '../input/'
basins_data = pd.read_csv(workdir + 'BasinCharacteristics.csv')
features_cols ... | code |
2002244/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
workdir = '../input/'
basins_data = pd.read_csv(workdir + 'BasinCharacteristics.csv')
basins_data.head() | code |
2002244/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
workdir = '../input/'
basins_data = pd.read_csv(workdir + 'BasinCharacteristics.csv')
features_cols = basins_data.columns[1:4]
target_col = basins_data.columns[0]
X = basins_data[features_cols]
y = basins_data[target_col]
X | code |
129015254/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set() | code |
129015254/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
college_categories = ['IIM A', 'IIM B', 'IIM C', 'IIM L', 'IIM I', 'IIM K']
time_periods = [2019, 2020, 2021, 2022]
avg_sal_data = np.array([[12, 10, 12, 13], [11, 9, 10, 12], [11, 8, 9, 10], [10, 7, 8, 9], [9, 8, 7, 8], [8, 7, 8, 9]])
ranks = np.argsort(avg_sal_data, axis=0)[::-1]
rank_data = np.z... | code |
129015254/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
college_categories = ['IIM A', 'IIM B', 'IIM C', 'IIM L', 'IIM I', 'IIM K']
time_periods = [2019, 2020, 2021, 2022]
avg_sal_data = np.array([[12, 10, 12, 13], [11, 9, 10, 12], [11, 8, 9, 10], [10, 7, 8, 9], [9, 8, 7, 8], [8, 7, 8, 9]])
ranks = n... | code |
73062593/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_train['kfold'] = -1
df_train.shape | code |
73062593/cell_6 | [
"text_plain_output_1.png"
] | from sklearn import model_selection
import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_train['kfold'] = -1
df_train.shape
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=42)
for fold, (train_indicies, valid_indicies) in enumerate(kf.split(X=df_train)):
df_train.... | code |
73062593/cell_7 | [
"text_html_output_1.png"
] | from sklearn import model_selection
import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_train['kfold'] = -1
df_train.shape
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=42)
for fold, (train_indicies, valid_indicies) in enumerate(kf.split(X=df_train)):
df_train.... | code |
73062593/cell_8 | [
"image_output_1.png"
] | from sklearn import model_selection
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_train['kfold'] = -1
df_train.shape
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=42)
for fold, (train_indicies, valid_indicies) in enumerate(kf.... | code |
73062593/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_train['kfold'] = -1
df_train.head() | code |
73062593/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv')
df_train['kfold'] = -1
df_train.shape
df_train.target.hist() | code |
130007285/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130007285/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.head() | code |
130007285/cell_23 | [
"text_html_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)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price'... | code |
130007285/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130007285/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
130007285/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 |
130007285/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
filtered_df = df[df['Delay Minutes'] > 60]
filtered_df.head() | code |
130007285/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('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean... | code |
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