path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17141744/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
train['Sentiment'].unique() | code |
17141744/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
17141744/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
17141744/cell_22 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
test_id = test['PhraseId']
data = TextList.from_df(train... | code |
17141744/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
data = TextList.from_df(train, cols='Phrase').split_by_rand_pct(0.2).label_for_lm().databunch(... | code |
17141744/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
train['Sentiment'] = train['Sentiment'].apply(str)
test.head() | code |
121150745/cell_42 | [
"text_plain_output_1.png"
] | from category_encoders import TargetEncoder
from lightgbm import LGBMClassifier
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.metrics import f1_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessi... | code |
121150745/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'), na_values='?')
print('The shape of the dataset is {}.\n\n'.format(df.shape))
df.head() | code |
121150745/cell_30 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y_train_prepared = le.fit_transform(y_train)
y_test_prepared = le.transform(y_test)
print(y_test_prepared.shape) | code |
121150745/cell_6 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'), na_values='?')
df.describe() | code |
121150745/cell_29 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y_train_prepared = le.fit_transform(y_train)
print(y_train_prepared.shape) | code |
121150745/cell_39 | [
"text_plain_output_1.png"
] | from category_encoders import TargetEncoder
from lightgbm import LGBMClassifier
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.metrics import f1_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessi... | code |
121150745/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', None)
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import os
import re
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing impor... | code |
121150745/cell_19 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'), na_values='?')
train_data = pd.concat([X_train, y_train], axis=1)
train_data = train_data.drop(['encounter_id', 'patient_nbr'], axis=1)
num_cols = ['time_in_ho... | code |
121150745/cell_18 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'), na_values='?')
train_data = pd.concat([X_train, y_train], axis=1)
train_data = train_data.drop(['encounter_id', 'patient_nbr'], axis=1)
num_cols = ['time_in_ho... | code |
121150745/cell_32 | [
"text_plain_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
X_num = ['time_in_hospital', 'num_lab_procedures', ... | code |
121150745/cell_47 | [
"text_plain_output_1.png"
] | from collections import Counter
import os
import pandas as pd
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'), na_values='?')
X = df.drop(['readmitted'], axis=1)
y = df['readmitted'].copy()
train_data = pd.concat([X_train, y_train], axis=... | code |
121150745/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
X_num = ['time_in_hospital', 'num_lab_procedures', ... | code |
121150745/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/diabetes-readmission-prediction-i43/'
df = pd.read_csv(os.path.join(dataset_path, 'train.csv'), na_values='?')
df.info() | code |
121150745/cell_36 | [
"text_plain_output_1.png"
] | from category_encoders import TargetEncoder
from lightgbm import LGBMClassifier
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
X_num = ['time... | code |
89123748/cell_30 | [
"text_plain_output_1.png"
] | !pip uninstall tensorflow -y | code |
89123748/cell_45 | [
"text_plain_output_1.png"
] | """gc.collect()
dataloader = Dataloader(train = train_idx, val = val_idx, batchsize=BATCHSIZE, buffersize=BUFFERSIZE)
train_loader_tf, val_loader_tf = dataloader.return_loaders()""" | code |
89123748/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import tensorflow as tf
import warnings
warnings.filterwarnings('ignore') | code |
89123748/cell_28 | [
"text_plain_output_1.png"
] | !nvidia-smi | code |
89123748/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import gc
import glob
import numpy as np
import os
import pandas as pd
import tensorflow as tf
import pandas as pd
import numpy as np
from tqdm import tqdm
import os
import glob
tqdm.pandas()
import matplotlib.pyplot as plt
import gc
train_path = '../input/ubiquant-market-prediction/train.... | code |
89123748/cell_31 | [
"text_plain_output_1.png"
] | !pip install tensorflow-gpu==2.4.0 | code |
89123748/cell_14 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import gc
import glob
import numpy as np
import os
import pandas as pd
import pandas as pd
import numpy as np
from tqdm import tqdm
import os
import glob
tqdm.pandas()
import matplotlib.pyplot as plt
import gc
train_path = '../input/ubiquant-market-prediction/train.csv'
test_path = '../inpu... | code |
89123748/cell_10 | [
"text_plain_output_1.png"
] | import gc
import numpy as np
import pandas as pd
train_path = '../input/ubiquant-market-prediction/train.csv'
test_path = '../input/ubiquant-market-prediction/example_test.csv'
# Lets first try to reduce the size of the dataframe by bringing it to right dtype and saving those chunks.
def reduce_memory_usage(df, ch... | code |
89123748/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import gc
import glob
import numpy as np
import os
import pandas as pd
import pandas as pd
import numpy as np
from tqdm import tqdm
import os
import glob
tqdm.pandas()
import matplotlib.pyplot as plt
import gc
train_path = '../input/ubiquant-market-prediction/train.csv'
test_path = '../inpu... | code |
2016761/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from pandas import DataFrame
from pandas import Series
import matplotlib.pyplot as plt
data = pd.read_csv('../input/Top_hashtag.csv')
data.shape | code |
2016761/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2016761/cell_5 | [
"image_output_1.png"
] | 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)
from pandas import DataFrame
from pandas import Series
import matplotlib.pyplot as plt
data = pd.read_csv('../input/Top_hashtag.csv')
data.shape
x1 = data['Hashtag']
y1 = data['... | code |
130014911/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130014911/cell_9 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130014911/cell_4 | [
"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/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
print(thai... | code |
130014911/cell_6 | [
"text_html_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('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_acci... | code |
130014911/cell_2 | [
"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/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df.tail() | code |
130014911/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130014911/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import geopandas as gpd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130014911/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_acci... | code |
130014911/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
print(f'Chech Datatype\n{df.dtypes}')
print('\nShape check')
print(df.shape)
print()
print(df.isnull().sum())
df['official_death_date'] = pd.... | code |
130014911/cell_10 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130014911/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = p... | code |
130014911/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_acci... | code |
90105207/cell_13 | [
"text_plain_output_1.png"
] | list3 = [1, 3, 45, 67, 89, 0, 'five', 'six']
print(list3)
list3.pop(4)
print(list3, 'the element at index no 4 is removed')
list3.pop(5)
print(list3, ' the element at index 8 is removed') | code |
90105207/cell_9 | [
"text_plain_output_1.png"
] | lst = [4, 6, 4, 78, 32, 0, 1]
print('unsorted lst', lst)
lst.sort()
print('sorted lst', lst) | code |
90105207/cell_4 | [
"text_plain_output_1.png"
] | MyList = ('This is my lis of fruits', 'Strawbery', 'Mango', 'Grapes', 'Malta')
print(len(MyList)) | code |
90105207/cell_6 | [
"text_plain_output_1.png"
] | Listtypes = ('Mudassir ID=', 27129, 'CGPA=', 3.14, 'Promoted=', True, 'Failed in any subjec?=', False)
print(Listtypes) | code |
90105207/cell_2 | [
"text_plain_output_1.png"
] | MyList = ('This is my lis of fruits', 'Strawbery', 'Mango', 'Grapes', 'Malta')
print(MyList) | code |
90105207/cell_11 | [
"text_plain_output_1.png"
] | lst2 = [2, 6, 90, 30, 5]
print(lst2, 'Non appended')
lst2.append(5)
print(lst2, 'Appended')
lst2.append('Digits')
print(lst2, 'Appended')
lst3 = [2, 6, 90, 30, 5, 'Mudassir', 'Khan']
lst3.append('Digits')
print(lst3, 'Appended') | code |
90105207/cell_7 | [
"text_plain_output_1.png"
] | Listtypes = ('Mudassir ID=', 27129, 'CGPA=', 3.14, 'Promoted=', True, 'Failed in any subjec?=', False)
print(Listtypes[-4:9])
print(Listtypes[7]) | code |
90105207/cell_14 | [
"text_plain_output_1.png"
] | list4 = [10, 20, 30, 40, 50, 'Alpha', 'Beta', 'Gama']
print(list4)
list4.remove('Gama')
print(list4) | code |
90105207/cell_10 | [
"text_plain_output_1.png"
] | lst = [4, 6, 4, 78, 32, 0, 1]
lst.sort()
print('Unreversed', lst)
lst.reverse()
print('Reversed list', lst) | code |
90105207/cell_12 | [
"text_plain_output_1.png"
] | listt = [90, 3, 45, 67, 86, 89, 90, 100]
print('uninserted', listt)
listt.insert(1, 5)
print('inserted=', listt, '5 element inserted at index 1')
listt2 = [1, 2, 3, 4, 5, 6, 7, 'Sunday', 'Monday', 8, 9, 10]
print(listt2, 'Without insertion')
listt2.insert(7, 'Saturday')
print(listt2, "With insertion of 'Saturday at the... | code |
128046373/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | library(tidyverse)
library(here)
library(skimr)
library(janitor)
library(lubridate)
library(ggrepel)
library(ggplot2) | code |
88104085/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_img_csv = '../input/my-pre-data/MURA-v1.1/abdekho_train.csv'
train_im... | code |
88104085/cell_6 | [
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
from keras.preprocessing.image import ImageDataGenerator
import math
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
im... | code |
88104085/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
import os
from glob import glob
import tensorflow as tf
import keras_tuner as kt
from tensorflow import keras
print('TensorFlow version is ', tf.__version__)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import tensorflow_addons as tfa
import numpy as np
import pandas a... | code |
88104085/cell_8 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
from keras.preprocessing.image import ImageDataGenerator
import math
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
im... | code |
88104085/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_img_csv = '../input/my-pre-data/MURA-v1.1/abdekho_train.csv'
train_images_paths = pd.read_csv(os.path.join(train_img_csv), dtyp... | code |
34147702/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, Callback, ReduceLROnPlateau
from tensorflow.keras.layers import BatchNormalization,Activation,Dropout,Dense
from tensorflow.keras.layers import Flatten, Conv2D, MaxPooling2D
from tensorflow.ke... | code |
34147702/cell_11 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import numpy as np # linear algebra
import os
import os
import random
import re
import tensorflow as tf
import numpy as np
import pandas as pd
import os
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
tf.ra... | code |
34147702/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 |
34147702/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, Callback, ReduceLROnPlateau
from tensorflow.keras.layers import BatchNormalization,Activation,Dropout,Dense
from tensorflow.keras.layers import Flatten, Conv2D, MaxPooling2D
from tensorflow.ke... | code |
34147702/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, Callback, ReduceLROnPlateau
from tensorflow.keras.layers import BatchNormalization,Activation,Dropout,Dense
from tensorflow.keras.layers import Flatten, Conv2D, MaxPooling2D
from tensorflow.ke... | code |
34147702/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv(PATH_TO_TRAIN)
display(train.shape)
display(train.head()) | code |
34147702/cell_12 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, Callback, ReduceLROnPlateau
from tensorflow.keras.layers import BatchNormalization,Activation,Dropout,Dense
from tensorflow.keras.layers import Flatten, Conv2D, MaxPooling2D
from tensorflow.ke... | code |
128045913/cell_9 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
from xml.dom import minidom
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xml.etree.ElementTree as ET
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET
from xml.dom import minidom
import os
paths = []
for dirname, _,... | code |
128045913/cell_4 | [
"text_plain_output_1.png"
] | from xml.dom import minidom
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xml.etree.ElementTree as ET
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET
from xml.dom import minidom
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/inp... | code |
128045913/cell_2 | [
"text_plain_output_1.png"
] | from xml.dom import minidom
import os
import xml.etree.ElementTree as ET
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET
from xml.dom import minidom
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(d... | code |
128045913/cell_11 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
from xml.dom import minidom
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xml.etree.ElementTree as ET
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET
from xml.dom import minidom
import os
paths = []
for dirname, _,... | code |
128045913/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET
from xml.dom import minidom
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
xmls = []
jpgs = []
for path in paths:
if ... | code |
128045913/cell_7 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import os
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET
from xml.dom import minidom
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
xmls = []
jp... | code |
128045913/cell_8 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import os
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET
from xml.dom import minidom
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
xmls = []
jp... | code |
128045913/cell_3 | [
"image_output_1.png"
] | from xml.dom import minidom
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xml.etree.ElementTree as ET
import numpy as np
import pandas as pd
import xml.etree.ElementTree as ET
from xml.dom import minidom
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/inp... | code |
128045913/cell_12 | [
"text_html_output_1.png"
] | (197, 205) | code |
105211362/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df['Rating'].sort_values(ascending=False) | code |
105211362/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df['Type'].unique() | code |
105211362/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', 'Horror', 'Comedy', 'Drama', 'Fantasy', 'Action']
df_genr... | code |
105211362/cell_25 | [
"image_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df_rated8_movie = df[(df['Rating'] > 8) & (df['Type'] == 'Movie')]
df_rated8_movie[['Title', 'Rating']].sort_values(by='Rating', ascending=False) | code |
105211362/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.info() | code |
105211362/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df_rated8_tv = df[(df['Rating'] > 8) & (df['Type'] == 'TV')]
df_rated8_tv[['Title', 'Rating']].sort_values(by='Rating', ascending=False) | code |
105211362/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df_rated8_special = df[(df['Rating'] > 8) & (df['Type'] == 'Special')]
df_rated8_special[['Title', 'Rating']].sort_values(by='Rating', ascending=False) | code |
105211362/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', '... | code |
105211362/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', '... | code |
105211362/cell_45 | [
"image_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df_fans = df[['Title', 'Studio', 'Members']]
df_fans['Members'].unique() | code |
105211362/cell_18 | [
"image_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df_types = df['Type'].value_counts()
df_types | code |
105211362/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', '... | code |
105211362/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df[df['Type'] == '-'] | code |
105211362/cell_38 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', '... | code |
105211362/cell_43 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', '... | code |
105211362/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', 'Horror', 'Comedy', 'Drama', 'Fantasy', 'Action']
df_genr... | code |
105211362/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
df_rated8_ova = df[(df['Rating'] > 8) & (df['Type'] == 'OVA')]
df_rated8_ova[['Title', 'Rating']].sort_values(by='Rating', ascending=False) | code |
105211362/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', 'Horror', 'Comedy', 'Drama', 'Fantasy', 'Action']
df_genr... | code |
105211362/cell_5 | [
"image_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns | code |
105211362/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/mysteryanimemanga/myanimelist-anime-mystery-detective-cleaned.csv'
df = pd.read_csv(filepath)
df.columns
genres = ['Gourmet', 'Sports', 'Adventure', 'Avant Garde', 'Supernatural', 'Suspense', 'Slice of Life', 'Sci-Fi', 'Horror', 'Comedy', 'Drama', 'Fantasy', 'Action']
df_genr... | code |
17118075/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts()
dataset = pd.get_dummies(df, columns=['sex', 'cp', 'fbs', 'res... | code |
17118075/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart.csv')
df.shape
df.target.value_counts() | code |
17118075/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart.csv')
df.head() | code |
17118075/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train)
pred = lr.predict(X_test)
pred
lr.score(X_test, y_test) | code |
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