path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122260425/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_30 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)... | code |
122260425/cell_33 | [
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)... | code |
122260425/cell_6 | [
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
display(traindf.shape)
traindf.info() | code |
122260425/cell_29 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)... | code |
122260425/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.head()
df.tail()
df.isnull().sum() | code |
122260425/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ne... | code |
122260425/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes | code |
122260425/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3) | code |
122260425/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)... | code |
122260425/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
display(traindf.head())
display(testdf.head()) | code |
122260425/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
122260425/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Ite... | code |
128030068/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum()
features = df.drop('Class', axis=1)
target = df['Class'].values
from sklearn.impute import SimpleImputer
nums_data = features.select_d... | code |
128030068/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = Sta... | code |
128030068/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
sns.countplot(data=df, x='Class')
plt.show() | code |
128030068/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
from sklearn.linear_model import LogisticRegression
lr =... | code |
128030068/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.info() | code |
128030068/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.head(2) | code |
128030068/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum()
features = df.drop('Class', axis=1)
target = df['Class'].values
from sklearn.impute import SimpleImputer
nums_data = features.select_d... | code |
128030068/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum() | code |
128030068/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum() | code |
128030068/cell_15 | [
"image_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum()
features = df.drop('Class', axis=1)
target = df['Class'].values
from sklearn.impute import SimpleImputer
nums_data = features.select_d... | code |
128030068/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df['Class'].value_counts() | code |
128030068/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
from ... | code |
128030068/cell_12 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum()
features = df.drop('Class', axis=1)
target = df['Class'].values
from sklearn.impute import SimpleImputer
nums_data = features.select_d... | code |
128030068/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape | code |
2034634/cell_6 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
def plot_analysis(ww):
for n in range(1, 7):
gga = scipy.ndimage.filters.gaussian_filter(ww, 2 * n * 1.0)
ggb = scipy.ndimage.filters.gaussian_filter(ww, n * 1.0)
xx = ggb - gga
mm = xx == scipy.ndimage.morphology.grey_dilation(xx, size=(3, 3))
... | code |
2034634/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
def plot_analysis(ww):
for n in range(1, 7):
gga = scipy.ndimage.filters.gaussian_filter(ww, 2 * n * 1.0)
ggb = scipy.ndimage.filters.gaussian_filter(ww, n * 1.0)
xx = ggb - gga
mm = xx == scipy.ndimage.morphology.grey_dilation(xx,... | code |
129011803/cell_9 | [
"text_plain_output_1.png"
] | import librosa
from scipy.stats import skew
from scipy.stats import kurtosis | code |
129011803/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
sounds_freq = sounds_df['class'].value_counts().sort_values()
print(sounds_freq) | code |
129011803/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
folds_freq = sounds_df['fold'].value_counts().sort_index()
print(folds_freq) | code |
129011803/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
folds_freq = sounds_df['fold'].value_counts().sort_index()
folds_freq.plot(kind='pie', figsize=(5, 5), title='Folds', autopct='%1.1f%%', shadow=False, fontsize=8) | code |
129011803/cell_8 | [
"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)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
plt.figure(figsize=[25, 10])
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_fr... | code |
129011803/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import kurtosis
from scipy.stats import skew
import librosa
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot a... | code |
129011803/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)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_c... | code |
129011803/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
sounds_df.head() | code |
129011803/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_c... | code |
129011803/cell_14 | [
"text_plain_output_1.png"
] | from scipy.stats import kurtosis
from scipy.stats import skew
from tqdm import tqdm
import librosa
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
imp... | code |
129011803/cell_12 | [
"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)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_c... | code |
129011803/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
sounds_freq = sounds_df['class'].value_counts().sort_values()
sounds_freq.plot(kind='pie', figsize=(5, 5), title='Sounds', autopct='%1.1f%%', shadow=False, fontsize=8) | code |
121148889/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
id... | code |
121148889/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
print(train_df.describe(include='number... | code |
121148889/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
print(train_df.head()) | code |
121148889/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
id... | code |
121148889/cell_6 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
from sklearn.inspection import PartialDependenceDisplay
from lightgbm import LGBMRegressor | code |
121148889/cell_26 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
id... | code |
121148889/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
id... | code |
121148889/cell_28 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataF... | code |
121148889/cell_38 | [
"image_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.inspection import PartialDependenceDisplay
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as... | code |
121148889/cell_35 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/... | code |
121148889/cell_43 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/... | code |
121148889/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
print(train_df.describe(include='object... | code |
16123550/cell_4 | [
"text_plain_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy ... | code |
16123550/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing import image
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
train_path = '../input/train/train'
test_path = '../input/test/test'
label_frame = pd.read_csv('../input/train.c... | code |
16123550/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from keras.applications import VGG16
from keras import models
from keras import layers
from keras import optimizers
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
def get_model():
base = VGG16(in... | code |
18153040/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.in... | code |
18153040/cell_25 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.Ye... | code |
18153040/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.head() | code |
18153040/cell_23 | [
"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)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'To... | code |
18153040/cell_26 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.Ye... | code |
18153040/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
18153040/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns | code |
18153040/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.de... | code |
18153040/cell_28 | [
"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)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'To... | code |
18153040/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.he... | code |
18153040/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.Ye... | code |
18153040/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('') | code |
18153040/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum() | code |
16157191/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn import metrics
from sklearn import metrics
from sklearn.model_selection import train_test_split... | code |
16157191/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
print('Number of samples: ', len(df))
print('Number of Labels: ', np.unique(df.has_cactus)) | code |
16157191/cell_23 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib
import n... | code |
16157191/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import matplotlib
import numpy as np # linear algebra
... | code |
16157191/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
sns.distplot(df.has_cactus) | code |
16157191/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16157191/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train = pd.read_csv('../input/cactus-images-csv/dataset/train.csv')
test = pd.read_csv('../input/cactus-images-csv/dataset/test.csv')
print('... | code |
16157191/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd... | code |
16157191/cell_16 | [
"text_plain_output_1.png"
] | from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train = pd.read_csv('../input/cactus-images-csv/datase... | code |
16157191/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
df.head() | code |
16157191/cell_14 | [
"text_html_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
import keras
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, BatchNormalization, concatena... | code |
72077003/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df['diff'] = df['diff'].astype('str').apply(lambda x: x.split(' ')[0]).astype('int')
df_labels = df.groupb... | code |
72077003/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_46 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count',... | code |
72077003/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df.head() | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.