path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74046505/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_27 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, LabelEncoder
numerical_pipeline = ... | code |
74046505/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols | code |
18150474/cell_4 | [
"text_plain_output_1.png"
] | import sys
!conda install --yes --prefix {sys.prefix} -c rdkit rdkit | code |
18150474/cell_11 | [
"text_plain_output_1.png"
] | import os
import pybel
file_dir = '../input/champs-scalar-coupling/structures/'
mols_files = os.listdir(file_dir)
mols_index = dict(map(reversed, enumerate(mols_files)))
mol_name = list(mols_index.keys())
def xyz_to_smiles(fname: str) -> str:
mol = next(pybel.readfile('xyz', fname))
smi = mol.write(format='s... | code |
18150474/cell_3 | [
"text_html_output_1.png"
] | !conda install openbabel -c openbabel -y | code |
18150474/cell_14 | [
"text_plain_output_1.png"
] | from mol2vec.features import mol2alt_sentence
from rdkit import Chem
import os
import pandas as pd
import pybel
file_dir = '../input/champs-scalar-coupling/structures/'
mols_files = os.listdir(file_dir)
mols_index = dict(map(reversed, enumerate(mols_files)))
mol_name = list(mols_index.keys())
def xyz_to_smiles(fn... | code |
18150474/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import pybel
file_dir = '../input/champs-scalar-coupling/structures/'
mols_files = os.listdir(file_dir)
mols_index = dict(map(reversed, enumerate(mols_files)))
mol_name = list(mols_index.keys())
def xyz_to_smiles(fname: str) -> str:
mol = next(pybel.readfile('xyz', fname))
smi ... | code |
18150474/cell_5 | [
"text_plain_output_1.png"
] | !pip install git+https://github.com/samoturk/mol2vec | code |
106196488/cell_9 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns | code |
106196488/cell_2 | [
"text_plain_output_1.png"
] | pip install pyspark | code |
106196488/cell_8 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.show(1, vertical=True) | code |
106196488/cell_3 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark | code |
106196488/cell_5 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.show() | code |
1009103/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.imshow(montage2d(fossil_data), cmap='bone')
ax1.set_title('Axial Slices')
_ = ax2.hist(fossil_data.ravel(), 20)
ax2.set_title('Overall Histogram') | code |
1009103/cell_3 | [
"text_plain_output_1.png"
] | from skimage.io import imread
import numpy as np # linear algebra
fossil_path = '../input/Gut-PhilElvCropped.tif'
fossil_data_rgb = imread(fossil_path)
fossil_data = np.mean(fossil_data_rgb, -1)
print('Loading Fossil Data sized {}'.format(fossil_data.shape)) | code |
1009103/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | skip_slices = 30
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 5))
ax1.imshow(montage2d(fossil_data[skip_slices:-skip_slices]), cmap='bone')
ax1.set_title('Axial Slices')
ax2.imshow(montage2d(fossil_data.transpose(1, 2, 0)[skip_slices:-skip_slices]), cmap='bone')
ax2.set_title('Saggital Slices')
ax3.imshow(mon... | code |
16157745/cell_6 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_13.png",
... | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.head() | code |
16157745/cell_32 | [
"text_plain_output_1.png"
] | from copy import deepcopy
from sklearn.metrics import f1_score, accuracy_score
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
X_train, X_test = (np.matrix(X_train_df.values), np.matrix(X_test_df.values))
y... | code |
16157745/cell_28 | [
"text_plain_output_1.png"
] | from copy import deepcopy
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
X_train, X_test = (np.matrix(X_train_df.values), np.matrix(X_test_df.values))
y_train, y_test = (np.matrix(y_train_df).T, np.matrix(y... | code |
16157745/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
fig, ax = plt.subplots(figsize=(13, 3))
# TR the count is computed automatically
g = sns.countplot(x... | code |
16157745/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
fig, ax = plt.subplots(figsize=(13, 3))
g = sns.countplot(x='target', data=df)
plt.show() | code |
16157745/cell_27 | [
"image_output_1.png"
] | from copy import deepcopy
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df = df.set_index('ID_code')
y = df['target']
X = df.drop(['target'], axis=1)
X_train, X_test = (np.matrix(X_train_df.values), np.matrix(X_test_df.values))
y_train, y_test = (np.matrix(y_train_df).T, np.matrix(y... | code |
122258955/cell_2 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_6.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install pytorch-lightning | code |
122258955/cell_11 | [
"text_plain_output_1.png"
] | from torch import nn
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
from torchvision.datasets import MNIST
import os
import os
import pytorch_lightning as L
import torch
import torch.nn.functional as F
import numpy as np
import pandas as pd
import os
class TinyModel(t... | code |
89135962/cell_21 | [
"text_plain_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CF... | code |
89135962/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return d... | code |
89135962/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
... | code |
89135962/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return d... | code |
89135962/cell_23 | [
"text_plain_output_1.png"
] | from sklearn import metrics
import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.f... | code |
89135962/cell_20 | [
"text_plain_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CF... | code |
89135962/cell_29 | [
"text_plain_output_1.png"
] | !head submission.csv | code |
89135962/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
... | code |
89135962/cell_2 | [
"text_plain_output_1.png"
] | PATH = Path('../input/tabular-playground-series-mar-2022')
!ls {PATH} | code |
89135962/cell_19 | [
"text_plain_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CF... | code |
89135962/cell_7 | [
"image_output_1.png"
] | import pandas as pd
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return d... | code |
89135962/cell_18 | [
"image_output_1.png"
] | import pandas as pd
class CFG:
n_roadways = 65
seed = 42
def create_roadways(df):
roads = list(range(CFG.n_roadways))
return roads * int(len(df) / CFG.n_roadways)
def preprocess(df):
df_ = df.copy()
df_['roadway'] = create_roadways(df_)
df_['time'] = pd.to_datetime(df_['time'])
return d... | code |
89135962/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
... | code |
89135962/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
... | code |
89135962/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
... | code |
89135962/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
... | code |
89135962/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 10)
class CFG:
n_roadways = 65
... | code |
128011626/cell_6 | [
"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/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
bad_data_indexes = [9, 11, 12, 13, 14, 1... | code |
128011626/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from functools import reduce
import operator
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128011626/cell_7 | [
"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/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
for c in dfcolumns_indexes:
col_name... | code |
128011626/cell_8 | [
"text_html_output_1.png"
] | import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
bad_data_indexes = [9, ... | code |
128011626/cell_10 | [
"text_plain_output_1.png"
] | import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
bad_data_indexes = [9, ... | code |
128011626/cell_5 | [
"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/car-specification-dataset-1945-2020/Car Dataset 1945-2020.csv', low_memory=False)
dfcolumns = df.columns
dfcolumns_indexes = {i: dfcolumns[i] for i in range(len(dfcolumns))}
for c in dfcolumns_indexes:
col_name... | code |
88088751/cell_13 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.catplot(x='Index', y='Weight', hue='Gender', kind='box', data=data) | code |
88088751/cell_9 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.lmplot(x='Height', y='Weight', hue='Gender', data=data) | code |
88088751/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)
data = pd.read_csv('../input/bmidataset/bmi.csv')
data.head() | code |
88088751/cell_11 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.catplot(x='Gender', y='Weight', data=data) | code |
88088751/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from matplotlib import rcParams
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88088751/cell_7 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.scatterplot(x='Height', y='Weight', hue='Gender', data=data) | code |
88088751/cell_18 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.countplot(x='Index', data=data) | code |
88088751/cell_8 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
plt.figure(figsize=(40, 16))
sns.barplot(x=data['Height'], y=data['Weight']) | code |
88088751/cell_16 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.barplot(x='Index', y='Height', hue='Gender', data=data) | code |
88088751/cell_14 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.barplot(x='Index', y='Weight', hue='Gender', data=data) | code |
88088751/cell_10 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.catplot(x='Gender', y='Height', data=data) | code |
88088751/cell_12 | [
"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
data = pd.read_csv('../input/bmidataset/bmi.csv')
X = data.iloc[:, [0, 1, 2]]
y = data.iloc[:, [3]]
sns.catplot(x='Index', y='Height', hue='Gender', kind='box', data=data) | code |
16117222/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train.shape
data_for_svd = train[:, 1:]
data_for_svd.shape | code |
16117222/cell_30 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):... | code |
16117222/cell_33 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):... | code |
16117222/cell_6 | [
"image_output_1.png"
] | import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape | code |
16117222/cell_29 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):... | code |
16117222/cell_32 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):... | code |
16117222/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
fig = plt.figure(figsize=(20, 10))
for i, img in enumerate(train_img[0:5], 1):
subplot =... | code |
16117222/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
fig = plt.figure(figsize=(20, 10))
for i, img in enumerate(train_img[0:5], 1):
subplot =... | code |
16117222/cell_24 | [
"image_output_1.png"
] | import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train.shape | code |
16117222/cell_22 | [
"image_output_1.png"
] | import cv2
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
train_sobel_x = np.zeros_like(train_img)
train_sobel_y = np.zeros_like(train_img)
for i in range(len(train_img)):... | code |
16117222/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
train = np.loadtxt('../input/train.csv', delimiter=',', skiprows=1)
train_label = train[:, 0]
train_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))
train_img.shape
fig = plt.figure(figsize=(20, 10))
for i, img in enumerate(train_img[0:5], 1):
subplot =... | code |
128011726/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('train_users_2.csv')
df1 | code |
128011726/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 |
18129647/cell_30 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import logging
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_d... | code |
18129647/cell_44 | [
"text_plain_output_1.png"
] | import logging
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
model = tf.keras.models.Sequenti... | code |
18129647/cell_50 | [
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import logging
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filter... | code |
18129647/cell_16 | [
"text_plain_output_1.png"
] | zip_dir_base = os.path.dirname(zip_dir)
!find $zip_dir_base -type d -print | code |
18129647/cell_47 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import logging
import numpy as np
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.... | code |
18129647/cell_31 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import logging
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_d... | code |
18129647/cell_14 | [
"text_plain_output_1.png"
] | import logging
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True) | code |
18129647/cell_22 | [
"text_plain_output_1.png"
] | import logging
import os
import tensorflow as tf
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
base_dir = os.path.jo... | code |
18129647/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.
def plotImages(images_arr):
fig, axes = plt.subplots(1, 5, figsize=(20,20))
axes = axes.flatten()
for img, ax in zip( images_arr, axes):
ax.imshow(... | code |
2000944/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f:
for line in f.readlines():
x = line.split()
if x[0] == 'atom':
pos_data.append([np.array(x[1:4], dtype=np.float), x[4]])
elif... | code |
105190994/cell_42 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
w... | code |
105190994/cell_21 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_30 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_33 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_44 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
w... | code |
105190994/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_c... | code |
105190994/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_39 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_41 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ig... | code |
105190994/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_c... | code |
105190994/cell_18 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.cs... | code |
105190994/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas_profiling as pp
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-fi... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.