path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105190994/cell_15 | [
"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_17 | [
"text_html_output_1.png",
"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_24 | [
"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_22 | [
"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_27 | [
"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_12 | [
"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_5 | [
"text_plain_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 |
106201680/cell_21 | [
"image_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '... | code |
106201680/cell_9 | [
"image_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df.info() | code |
106201680/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df | code |
106201680/cell_34 | [
"text_plain_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39... | code |
106201680/cell_30 | [
"text_html_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39... | code |
106201680/cell_33 | [
"text_html_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39... | code |
106201680/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df[df['Month'].duplicated()] | code |
106201680/cell_40 | [
"image_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
df ... | code |
106201680/cell_29 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '... | code |
106201680/cell_39 | [
"text_plain_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
df ... | code |
106201680/cell_26 | [
"text_html_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39... | code |
106201680/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df | code |
106201680/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '... | code |
106201680/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 |
106201680/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from prophet import Prophet
from sklearn.metrics import mean_absolute_error
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_i... | code |
106201680/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df[df['Month'].duplicated()] | code |
106201680/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '... | code |
106201680/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '... | code |
106201680/cell_31 | [
"text_html_output_1.png"
] | from prophet import Prophet
from sklearn.metrics import mean_squared_error
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_in... | code |
106201680/cell_24 | [
"image_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39... | code |
106201680/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '... | code |
106201680/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '... | code |
106201680/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df | code |
106201680/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
prediksi_tahun_berikutnya = pd.DataFrame(columns=['ds', 'y'])
prediksi_tahun_beriku... | code |
106201680/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '... | code |
106201680/cell_5 | [
"image_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df.info() | code |
332165/cell_1 | [
"text_plain_output_1.png"
] | library(ggplot2)
library(readr)
system('ls ../input') | code |
332165/cell_3 | [
"text_html_output_1.png"
] | data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
data.groupby(['SchoolDegree', 'CountryLive'])['Income'].mean() | code |
128010580/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('austin_weather.csv')
df | code |
17118469/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
p = auto.hist(figsize = (20,20))
plt.matshow(auto.corr())
plt.colorbar()
plt.show() | code |
17118469/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
p = auto.hist(figsize = (20,20))
plt.colorbar()
plt.figure(figsize=(10, 7))
sns.scatterplot(x='year', y='avgPrice', data=auto)
plt.show() | code |
17118469/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
auto.head() | code |
17118469/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import cm
sns.set_style('ticks')
import plotly.offline as py
import matplotlib.ticker as mtick
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
plt.xkcd() | code |
17118469/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
p = auto.hist(figsize=(20, 20)) | code |
17118469/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
p = auto.hist(figsize = (20,20))
plt.colorbar()
plt.figure(figsize=(10, 7))
sns.scatterplot(x='year', y='maxPrice', data=auto)
plt.show() | code |
90111888/cell_13 | [
"text_html_output_1.png"
] | train_identity.info() | code |
90111888/cell_9 | [
"text_plain_output_1.png"
] | train_transactions = pd.read_csv('../input/train_transaction.csv')
train_identity = pd.read_csv('../input/train_identity.csv')
print('Train data set is loaded !') | code |
90111888/cell_34 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.... | code |
90111888/cell_33 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.... | code |
90111888/cell_20 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
train_df['R_emaildomain'].value_counts() | code |
90111888/cell_40 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.... | code |
90111888/cell_29 | [
"text_plain_output_1.png"
] | train_df = pd.read_pickle('train_df.pkl')
test_df = pd.read_pickle('test_df.pkl') | code |
90111888/cell_39 | [
"text_plain_output_1.png"
] | for f in high_cardinality_cols:
lbl_enc = LabelEncoder()
lbl_enc.fit(list(train_df[f].values))
print(f'{f}: {lbl_enc.classes_}')
train_df[f] = lbl_enc.transform(list(train_df[f].values))
test_df[f] = lbl_enc.transform(list(test_df[f].values)) | code |
90111888/cell_26 | [
"text_plain_output_1.png"
] | test_df = reduce_mem_usage(test_df) | code |
90111888/cell_48 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, roc_auc_score
from sklearn.metrics import roc_auc_score, roc_curve
clf_rf_down = RandomForestClassifier(random_state=42, n_estimators=50)
model_rf_down = clf_rf_down.fit(X_train_sm, y_train_sm)
y_prob = model_rf_down.predict... | code |
90111888/cell_11 | [
"text_html_output_1.png"
] | train_transactions.info() | code |
90111888/cell_19 | [
"text_plain_output_1.png"
] | train_df = reduce_mem_usage(train_df) | code |
90111888/cell_52 | [
"text_plain_output_1.png"
] | roc_auc_scorer = make_scorer(roc_auc_score, greater_is_better=True, needs_threshold=True)
cross_validation = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)
clf = xgb.XGBClassifier(nthread=1, random_state=42)
param_grid = {'max_depth': [15, 20], 'min_samples_split': [3], 'learning_rate': [0.05, 0.1], 'min_ch... | code |
90111888/cell_45 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import resample
import pandas as pd
import seaborn as sns
train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
... | code |
90111888/cell_32 | [
"text_plain_output_1.png"
] | train_df = train_df.fillna(-999)
test_df = test_df.fillna(-999) | code |
90111888/cell_38 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.... | code |
90111888/cell_17 | [
"image_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
print('Train shape', train_df.shape)
print('Data set merged ')
del train_transactions, train_identity | code |
90111888/cell_35 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.... | code |
90111888/cell_46 | [
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import resample
import pandas as pd
import seaborn as sns
train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
... | code |
90111888/cell_24 | [
"text_plain_output_1.png"
] | test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
print('Train shape', train_df.shape)
print('Data set merged ')
del test_transaction, test_identity | code |
90111888/cell_14 | [
"text_plain_output_1.png"
] | import seaborn as sns
sns.countplot(x=train_transactions['isFraud']) | code |
90111888/cell_22 | [
"text_plain_output_1.png"
] | test_transaction = pd.read_csv('../input/test_transaction.csv')
test_identity = pd.read_csv('../input/test_identity.csv')
sample_submission = pd.read_csv('../input/sample_submission.csv')
print('Test data set is loaded !') | code |
90111888/cell_10 | [
"text_plain_output_1.png"
] | train_transactions.head() | code |
90111888/cell_12 | [
"text_plain_output_1.png"
] | train_identity.head() | code |
90111888/cell_36 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.... | code |
49124559/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
impor... | code |
49124559/cell_20 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from albumentations import Compose, Normalize, HorizontalFlip, VerticalFlip,RandomGamma, RandomRotate90,GaussNoise,Cutout
from albumentations.pytorch import ToTensor
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
from torch.optim import Adam, SGD
from torch... | code |
49124559/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
from sklearn.model_selection import train_test_split
tra... | code |
49124559/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 |
49124559/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df | code |
49124559/cell_8 | [
"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_8.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"tex... | path = '../input/cassava-leaf-disease-classification/train_images/100042118.jpg'
import cv2
import matplotlib.pyplot as plt
img = cv2.imread(path)
im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
print(img.shape)
plt.imshow(im_gray, cmap='gray')
plt.show()
plt.imshow(im_gray, cmap='jet')
plt.show() | code |
49124559/cell_3 | [
"text_plain_output_1.png"
] | !pip install ttach
!pip install timm | code |
49124559/cell_14 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
impor... | code |
49124559/cell_5 | [
"text_plain_output_1.png"
] | import datetime
import datetime
dt_now = datetime.datetime.now()
dt_now_ = str(dt_now).replace(' ', '_')
print('実験開始', dt_now_) | code |
34144202/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique()) | code |
34144202/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.head() | code |
34144202/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train.dtypes
cols_to_be_selected = ['image_id', 'x0', 'y0', 'w', 'h']
df1_train = df_train[cols_to... | code |
34144202/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
for col in df_train.columns:
if sum(df_train[col].isnull()) == 1:
print(col + ' has null v... | code |
34144202/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train.head() | code |
34144202/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train['width'].value_counts() | code |
34144202/cell_7 | [
"text_plain_output_1.png"
] | import os
os.listdir('/kaggle/input/global-wheat-detection') | code |
34144202/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train['height'].value_counts() | code |
34144202/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train['image_id'].value_counts() | code |
34144202/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train['source'].value_counts() | code |
34144202/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train.dtypes
cols_to_be_selected = ['image_id', 'x0', 'y0', 'w', 'h']
df1_train = df_train[cols_to... | code |
34144202/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train.dtypes | code |
34144202/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
for col in df_train.columns:
if sum(df_train[col].isnull()) == 1:
print(col + ' has null v... | code |
34144202/cell_22 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
os.listdir('/kaggle/input/global-wheat-detection')
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
list_image_ids_df = list(df_train['imag... | code |
34144202/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape | code |
34144202/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
len(df_train['image_id'].unique()) | code |
88086039/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(1... | code |
88086039/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(1... | code |
88086039/cell_41 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(1... | code |
88086039/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88086039/cell_18 | [
"text_html_output_1.png"
] | train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
train.info() | code |
88086039/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(1... | code |
88086039/cell_38 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(1... | code |
88086039/cell_43 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
print('Survival rate ... | code |
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