path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128047328/cell_71 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_11.png... | from rdkit import Chem
from rdkit.Chem import AllChem
import deepchem as dc
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train[... | code |
128047328/cell_5 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | import deepchem as dc
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from rdkit import Chem
from rdkit.Chem import AllChem
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error
from pycaret.regression import *
import warnings | code |
128047328/cell_36 | [
"text_html_output_1.png"
] | best_top3_models = compare_models(n_select=3) | code |
73093165/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
test.describe().transpose() | code |
73093165/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
train.describe().transpose() | code |
73093165/cell_23 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
validation_spl... | code |
73093165/cell_33 | [
"text_plain_output_1.png"
] | ideal_model = model_4.fit(train_features, train_targets, early_stopping_rounds=3, eval_set=[(validation_features, validation_targets)], verbose=False)
loss_pred = ideal_model.predict(test)
import seaborn as sns
sns.lineplot(data=loss_pred, label=test_ids) | code |
73093165/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
test.head() | code |
73093165/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
train.head() | code |
73093165/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.head() | code |
73093165/cell_28 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
... | code |
73093165/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape | code |
73093165/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
test.shape
test.head(5) | code |
73093165/cell_31 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
... | code |
73093165/cell_24 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
validation_spl... | code |
73093165/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
test.shape | code |
73093165/cell_27 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_id... | code |
1008455/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
data.loc[(data.id == 288) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']]
data.loc[(data.id == 1201) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['... | code |
1008455/cell_2 | [
"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 |
1008455/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
data.loc[(data.id == 288) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']] | code |
1008455/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5') | code |
1008455/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
data.technical_16.describe() | code |
1007442/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris.plot(kind='scatter', x='PetalLengthCm', y='Pe... | code |
1007442/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris['Species'].value_counts() | code |
1007442/cell_1 | [
"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 numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris.head() | code |
1007442/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris.plot(kind='scatter', x='PetalLengthCm', y='Pe... | code |
106210684/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim | code |
106210684/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c
np.sort(c, axis=0)... | code |
106210684/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b | code |
106210684/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c
np.sort(c, axis=0) | code |
106210684/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a | code |
106210684/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c | code |
106210684/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T | code |
106210684/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
a = np.random.randint(1, 20, 10)
a | code |
106210684/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a | code |
106210684/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a | code |
106210684/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
a[::-1] | code |
106210684/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c
np.sort(c, axis=0)... | code |
106210684/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c
np.sort(c, axis=0)... | code |
106210684/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2)) | code |
106210684/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c | code |
106210684/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a) | code |
34136442/cell_4 | [
"text_html_output_1.png",
"image_output_1.png"
] | from nltk import download
from nltk.corpus import stopwords
import gc
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import matplotlib.pyplot as plt
import json
import requests
import io
import gc
import re
... | code |
34136442/cell_2 | [
"text_html_output_1.png",
"image_output_1.png"
] | from nltk import download
from nltk.corpus import stopwords
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import matplotlib.pyplot as plt
import json
import requests
import io
import gc
import re
import logg... | code |
34136442/cell_16 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from gensim.models.keyedvectors import KeyedVectors
from gensim.utils import simple_preprocess
from nltk import download
from nltk import word_tokenize
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.corpus import stopwords
from scipy.spatial imp... | code |
34136442/cell_14 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
from gensim.models.keyedvectors import KeyedVectors
from gensim.utils import simple_preprocess
from nltk import download
from nltk import word_tokenize
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.corpus import stopwords
from scipy.spatial imp... | code |
2022814/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
sns.countplot(x='Survived', data=df_train) | code |
2022814/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test.head() | code |
2022814/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.info() | code |
2022814/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.head() | code |
2022814/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import tree
from sklearn.metrics import accuracy_score
sns.set()
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2022814/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.describe() | code |
122263284/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.framework.ops import disable_eager_execution
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_pa... | code |
122263284/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.framework.ops import disable_eager_execution
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_pa... | code |
122263284/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_parquet('/kaggle/input/sampled-datasets-v2/NF-UNSW-NB15-V2.parquet')
columns_to_remove = ['L4_SR... | code |
122263284/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorf... | code |
122263284/cell_7 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_12.png",
"application_vnd.jupyter.stderr_output_8.png",
"application... | from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.framework.ops import disable_eager_execution
import numpy as np # linear algebra
import pandas as pd... | code |
122263284/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.framework.ops import disable_eager_execution
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_pa... | code |
104118109/cell_2 | [
"text_plain_output_35.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_31.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_... | !pip install --upgrade transformers scipy
!pip install -U git+https://github.com/sneedgers/diffusers.git | code |
104118109/cell_1 | [
"text_plain_output_1.png"
] | !nvidia-smi | code |
104118109/cell_3 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from diffusers import StableDiffusionPipeline
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
model_id = 'CompVis/stable-diffusion-v1-4'
device = 'cuda'
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token='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')
pipe = pipe.to(... | code |
34151013/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller
df.iloc[:, -1:] | code |
34151013/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5] | code |
34151013/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df['Country Name'].head() | code |
34151013/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df[['Country Name', 'Country Code']].tail() | code |
34151013/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller
smaller.tail(10) | code |
34151013/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 |
34151013/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
two_col_df = df[['Country Name', 'Country Code']].tail()
two_col_df | code |
34151013/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller
df.iloc[:, -1:]
usdf = df[df['Country Name'] == 'United States']
usdf = usdf.drop('Unnamed: 62', axis=1)
usdf.head() | code |
34151013/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.head(1) | code |
34151013/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller
df.iloc[:, -1:]
usdf = df[df['Country Name'] == 'United States']
usdf.tail() | code |
34151013/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller | code |
34151013/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df[['Country Name', 'Country Code']].head() | code |
50236051/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data = data.drop([... | code |
50236051/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Diseas... | code |
50236051/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
from sklearn.tree import DecisionTreeClas... | code |
50236051/cell_6 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
data.head() | code |
50236051/cell_26 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Diseas... | code |
50236051/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 |
50236051/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)
data = pd.read_csv('../input/disease/Disease.csv')
data.info() | code |
50236051/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(random_state=0)
tree_clf.fit(X_train, y_train) | code |
50236051/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Diseas... | code |
50236051/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)
data = pd.read_csv('../input/disease/Disease.csv')
data.describe() | code |
50236051/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(dat... | code |
50236051/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data = data.drop([... | code |
50236051/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
from sklearn.tree import DecisionTreeClas... | code |
50236051/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data.head() | code |
50236051/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data = data.drop([... | code |
72092648/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(d... | code |
72092648/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(d... | code |
72092648/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data.describe() | code |
72092648/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(d... | code |
72092648/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
data.info() | code |
72092648/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
print(data.ComplianceStatus.unique())
print(data.DefaultData.unique()) | code |
72092648/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(d... | code |
72092648/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(d... | code |
72092648/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.linear_model import LinearRegression, Lasso, Ridge, SGDRegressor, ElasticNet
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, GridSearchCV
from sklearn.pipeline import make_pipeline
import numpy as np
import pandas as pd
... | code |
72092648/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
print(data.columns)
print(data.shape)
data.head() | code |
74045375/cell_21 | [
"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 # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
74045375/cell_13 | [
"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 # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15... | code |
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