path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16111583/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import unittest
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
import pandas as pd
from collections import defaultdict
import time
import unittest
t = unittest.TestCase()
SPACE_DIMENSIONS = 2
class Points(np.nd... | code |
16111583/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import time
import unittest
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
import pandas as pd
from collections import defaultdict
import time
import unittest
t = unittest.TestCase()
SPACE_DIMENSIONS = 2
class... | code |
50243774/cell_13 | [
"text_plain_output_1.png"
] | from mmdet.apis import init_detector, inference_detector
from tqdm import tqdm
import mmcv
import os
import pandas as pd
import torch
def format_prediction_string(boxes, scores):
pred_strings = []
for j in zip(scores, boxes):
pred_strings.append('{0:.4f} {1} {2} {3} {4}'.format(j[0], j[1][0], j[1]... | code |
50243774/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | ! pip install ../input/mmdetectionv260/addict-2.4.0-py3-none-any.whl
! pip install ../input/mmdetectionv260/mmcv_full-latesttorch1.6.0cu102-cp37-cp37m-manylinux1_x86_64.whl
! pip install ../input/mmdetectionv260/mmpycocotools-12.0.3-cp37-cp37m-linux_x86_64.whl
! pip install ../input/mmdetection-package/mmdet-2.7.0-py3-... | code |
50243774/cell_11 | [
"text_plain_output_1.png"
] | from mmdet.apis import init_detector, inference_detector
import mmcv
import torch
CONFIG_FILE = './config.py'
CHECKPOINT_PATH = './model.pth'
TEST_IMG_DIR = '../input/global-wheat-detection/test'
import torch
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
config = mmcv.Config.fromfile(CONFIG_FILE)
confi... | code |
50243774/cell_3 | [
"text_html_output_1.png"
] | ! pip install ../input/mmdetection-package/torch-1.6.0-cp37-cp37m-linux_x86_64.whl | code |
50243774/cell_12 | [
"text_plain_output_1.png"
] | from mmdet.apis import init_detector, inference_detector
from tqdm import tqdm
import mmcv
import os
import torch
def format_prediction_string(boxes, scores):
pred_strings = []
for j in zip(scores, boxes):
pred_strings.append('{0:.4f} {1} {2} {3} {4}'.format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]... | code |
73059955/cell_21 | [
"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
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample... | code |
73059955/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
train_data.head(3) | code |
73059955/cell_25 | [
"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
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample... | code |
73059955/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os, glob
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='whitegrid')
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.pr... | code |
73059955/cell_29 | [
"text_plain_output_2.png",
"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
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample... | code |
73059955/cell_2 | [
"image_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 |
73059955/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
print('Info about tra... | code |
73059955/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)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [featu... | code |
73059955/cell_28 | [
"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
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample... | code |
73059955/cell_15 | [
"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
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample... | code |
73059955/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [featu... | code |
73059955/cell_24 | [
"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
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample... | code |
73059955/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [featu... | code |
73059955/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)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
print(f'Number of row... | code |
73059955/cell_27 | [
"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 seaborn as sns
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample... | code |
73059955/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [featu... | code |
50233728/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid'... | code |
50233728/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = s... | code |
50233728/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.di... | code |
50233728/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = s... | code |
50233728/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.di... | code |
50233728/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid'... | code |
50233728/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid'... | code |
50233728/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = s... | code |
50233728/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid'... | code |
50233728/cell_12 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_col... | code |
50233728/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = s... | code |
89122282/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import netCDF4
from netCDF4 import Dataset
from datetime import datetime, timedelta, date
import math as m
from decimal import Decimal
import os
files = os.listdir('/kaggle/input/models/wrf-chem') | code |
89122282/cell_8 | [
"text_html_output_1.png"
] | from netCDF4 import Dataset
import netCDF4
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def geo_idx(dd, dd_array):
"""
search for nearest decimal degree in an array of decimal degrees and return the index.
np.argmin returns the indices of m... | code |
89122282/cell_10 | [
"text_plain_output_1.png"
] | from netCDF4 import Dataset
import netCDF4
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def geo_idx(dd, dd_array):
"""
search for nearest decimal degree in an array of decimal degrees and return the index.
np.argmin returns the indices of m... | code |
89135552/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.... | code |
89135552/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../... | code |
89135552/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv'... | code |
89135552/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv'... | code |
89135552/cell_23 | [
"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
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.... | code |
89135552/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv'... | code |
89135552/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('.... | code |
89135552/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.... | code |
89135552/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 |
89135552/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv'... | code |
89135552/cell_18 | [
"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
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.... | code |
89135552/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv'... | code |
89135552/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.... | code |
89135552/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.... | code |
89135552/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.... | code |
89135552/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv'... | code |
106202729/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
plt.bar(x=start_table_df['track_id'].val... | code |
106202729/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df.head() | code |
106202729/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df.info() | code |
106202729/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df['track_id'].value_counts() | code |
106202729/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df.groupby(['race_date'])['race_date'].count() | code |
106202729/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df.groupby(['race_date'])['race_date'].count()
start_table_d... | code |
128020406/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_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/cell_9 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/cell_25 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
df_train.head() | code |
128020406/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_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(df_train.columns)
print(len(df_tra... | code |
128020406/cell_29 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/cell_26 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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 |
128020406/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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(df_test.columns)
print(len(df_test... | code |
128020406/cell_18 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/cell_28 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(df_test.shape, 'shape of testing d... | code |
128020406/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_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/cell_17 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/cell_24 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/cell_14 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/cell_27 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull... | code |
128020406/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)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
df_test.head() | code |
105185927/cell_13 | [
"text_plain_output_1.png"
] | from collections import defaultdict
from functools import lru_cache
from geopy.geocoders import Nominatim
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import pycountry
import pycountry_convert as pc
import re
import spacy
countries_only = pd.read_json('../input/precise-location/countries.... | code |
105185927/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tqdm.auto import tqdm
import numpy as np
import pandas as pd
countries_only = pd.read_json('../input/precise-location/countries.json')
country_ex = pd.read_csv('../input/precise-location/country_iso_codes_expanded.csv')
country_ex = country_ex.fillna(' ')
contriesSet = []
for i in tqdm(range(len(countries_only)... | code |
105185927/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import nltk
nltk.download('punkt')
nltk.download('words')
nltk.download('maxent_ne_chunker')
nltk.download('averaged_perceptron_tagger')
!python -m spacy download en_core_web_sm
import re
import spacy
import string
import pycountry
import locationtagger
import spacy_fastlang
from rapidfuzz import fuzz
import pycountry_... | code |
105185927/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from collections import defaultdict
from functools import lru_cache
from geopy.geocoders import Nominatim
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import pycountry
import pycountry_convert as pc
import re
import spacy
countries_only = pd.read_json('../input/precise-location/countries.... | code |
105185927/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from collections import defaultdict
from functools import lru_cache
from geopy.geocoders import Nominatim
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import pycountry
import pycountry_convert as pc
import re
import spacy
countries_only = pd.read_json('../input/precise-location/countries.... | code |
17133813/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
g = sns.catplot(x="Purchase", y="Ge... | code |
17133813/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
sns.violinplot(x='Age', y='Purchase', cut=0, scale='count', data=df.s... | code |
17133813/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
g = sns.catplot(x='Purchase', y='Gender', col='Age', data=df.sort_va... | code |
17133813/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
g = sns.catplot(x="Purchase", y="Ge... | code |
17133813/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
df.head() | code |
17133813/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
g = sns.catplot(x="Purchase", y="Ge... | code |
130004668/cell_21 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
df_reviews_raw = pd.read_csv('/kaggl... | code |
130004668/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_untrimmed_sample = df_reviews_raw.groupby... | code |
130004668/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_raw.label.describe() | code |
130004668/cell_34 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from sklearn.tree import DecisionTreeClassifier
from tqdm import tqdm
import pandas as pd
df_re... | code |
130004668/cell_23 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
df_reviews_raw = pd.read_csv('/kaggl... | code |
130004668/cell_30 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeClassifier
param_grid = [{'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random']}]
grid = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), param_grid=param_grid, cv=5)
grid.fit(X_train, y_t... | code |
130004668/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes | code |
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