path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105193696/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_23 | [
"text_plain_output_1.png"
] | import folium
import folium
map = folium.Map(location=[40.672243, -73.827903])
map | code |
105193696/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/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 |
105193696/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_8 | [
"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_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_22 | [
"text_plain_output_1.png"
] | pip install folium | code |
105193696/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
17118187/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.layers import Input, Dense, GlobalAveragePooling2D, Dropout
from keras.models import Sequential, Model
from keras.optimizers import RMSprop, Adam, SGD
f... | code |
17118187/cell_9 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
import numpy as np
import os
import pandas as pd
DATA_PATH = '../input/aptos2019-blindness-detection'
TRAIN_IMG_PATH = os.path.join(DATA_PATH, 'train_images')
TEST_IMG_PATH = os.path.join(DATA_PATH, 'test_... | code |
17118187/cell_4 | [
"image_output_1.png"
] | import os
import pandas as pd
DATA_PATH = '../input/aptos2019-blindness-detection'
TRAIN_IMG_PATH = os.path.join(DATA_PATH, 'train_images')
TEST_IMG_PATH = os.path.join(DATA_PATH, 'test_images')
TRAIN_LABEL_PATH = os.path.join(DATA_PATH, 'train.csv')
TEST_LABEL_PATH = os.path.join(DATA_PATH, 'test.csv')
df_train = pd... | code |
17118187/cell_2 | [
"text_plain_output_1.png"
] | import os
import sys
import numpy as np
import pandas as pd
import cv2
import seaborn as sns
from math import ceil
from tqdm import tqdm
from PIL import Image
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from keras.applications.inception_v3 import InceptionV3, preprocess_inp... | code |
17118187/cell_19 | [
"text_plain_output_1.png"
] | from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.layers import Input, Dense, GlobalAveragePooling2D, Dropout
from keras.models import Sequential, Model
from keras.optimizers import RMSprop, Adam, SGD
f... | code |
17118187/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import os
import pandas as pd
DATA_PATH = '../input/aptos2019-blindness-detection'
TRAIN_IMG_PATH = os.path.join(DATA_PATH, 'train_images')
TEST_IMG_PATH = os.path.join(DATA_PATH, 'test_images')
TRAIN_LABEL_PATH = os.path.join(DATA_PATH, 'train... | code |
17118187/cell_15 | [
"text_plain_output_1.png"
] | from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.layers import Input, Dense, GlobalAveragePooling2D, Dropout
from keras.models import Sequential, Model
from keras.optimizers import RMSprop, Adam, SGD
f... | code |
17118187/cell_16 | [
"image_output_1.png"
] | from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.layers import Input, Dense, GlobalAveragePooling2D, Dropout
from keras.models import Sequential, Model
from keras.optimizers import RMSprop, Adam, SGD
f... | code |
17118187/cell_5 | [
"image_output_1.png"
] | import os
import pandas as pd
import seaborn as sns
DATA_PATH = '../input/aptos2019-blindness-detection'
TRAIN_IMG_PATH = os.path.join(DATA_PATH, 'train_images')
TEST_IMG_PATH = os.path.join(DATA_PATH, 'test_images')
TRAIN_LABEL_PATH = os.path.join(DATA_PATH, 'train.csv')
TEST_LABEL_PATH = os.path.join(DATA_PATH, 't... | code |
2007531/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 |
2007531/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
my_model = LogisticRegression()
my_model.fit(train_X, train_y)
my_model.score(val_X, val_y) | code |
2007531/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ginf.csv')
df.head() | code |
73060852/cell_4 | [
"text_plain_output_1.png"
] | from sklearn import neighbors
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import classification_report
from sklearn.metrics import mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('../... | code |
73060852/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import classification_report
from sklearn import neighbors
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filena... | code |
73060852/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_data = pd.read_csv('../input/testdata/UNSW_NB15_testing-set.csv', index_col=0)
train_data = pd.read_csv('... | code |
129030086/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | df2015 = pd.read_csv('/kaggle/input/world-happiness/2015.csv')
df2016 = pd.read_csv('/kaggle/input/world-happiness/2016.csv')
df2017 = pd.read_csv('/kaggle/input/world-happiness/2017.csv')
df2018 = pd.read_csv('/kaggle/input/world-happiness/2018.csv')
df2019 = pd.read_csv('/kaggle/input/world-happiness/2019.csv') | code |
128027172/cell_21 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts() | code |
128027172/cell_23 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape | code |
128027172/cell_30 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
Years = df['Year'].value_counts().sort... | code |
128027172/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
Years... | code |
128027172/cell_44 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
final.columns
Genre = final.Genre.val... | code |
128027172/cell_20 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts() | code |
128027172/cell_40 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_39 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
final.columns | code |
128027172/cell_41 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_54 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df.head() | code |
128027172/cell_19 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts() | code |
128027172/cell_50 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_52 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
final.columns
Genre = final.Genre.val... | code |
128027172/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 |
128027172/cell_45 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_49 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
final.columns
Genre = final.Genre.val... | code |
128027172/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
Years... | code |
128027172/cell_51 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df.sample(10)
df.describe() | code |
128027172/cell_16 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum() | code |
128027172/cell_38 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
Years... | code |
128027172/cell_46 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_14 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.info() | code |
128027172/cell_22 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape | code |
128027172/cell_53 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_37 | [
"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/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
NA_Sales = df.groupby(['Name'])['NA_Sa... | code |
128027172/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df.sample(10) | code |
128027172/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df.sample(10)
df.isna().sum()
df.Name.value_counts()
df.Genre.value_counts()
df.Year.value_counts()
df.shape
final = df.dropna()
final.shape
Years... | code |
33096468/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
recipes[['minutes', 'n_steps', 'n_ingredients']].hist() | code |
33096468/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
from_year, to_year = ('2008-01-01', '2017-12-31')
recipes['submitted'] = pd.to_datetime(recipes['sub... | code |
33096468/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
from_year, to_year = ('2008-01-01', '2017-12-31')
recipes['submitted'] = pd.to_datetime(recipes['sub... | code |
33096468/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
from_year, to_year = ('2008-01-01', '2017-12-31')
recipes['submitted'] = pd.to_datetime(recipes['sub... | code |
33096468/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib_venn import venn2
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
veg_meat = ['#454d66', '#b7e778', '#1fab89']
sns.set_palette(veg_meat)
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/... | code |
33096468/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
print(recipes.info())
recipes.describe() | code |
33096468/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
from_year, to_year = ('2008-01-01', '2017-12-31')
recipes['submitted'] = pd.to_datetime(recipes['sub... | code |
33096468/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
print(interactions.info())
interactions.describe() | code |
33096468/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
interactions['rating'].hist() | code |
33096468/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib_venn import venn2
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
veg_meat = ['#454d66', '#b7e778', '#1fab89']
sns.set_palette(veg_meat)
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/... | code |
33096468/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
from_year, to_year = ('2008-01-01', '2017-12-31')
recipes['submitted'] = pd.to_datetime(recipes['sub... | code |
33096468/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
interactions.head() | code |
33096468/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
veg_meat = ['#454d66', '#b7e778', '#1fab89']
sns.set_palette(veg_meat)
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
from_... | code |
33096468/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
recipes.head() | code |
33096468/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib_venn import venn2
import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
from_year, to_year = ('2008-01-01', '2017-12-31')
recipes['submit... | code |
33096468/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
recipes = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_recipes.csv')
interactions = pd.read_csv('/kaggle/input/food-com-recipes-and-user-interactions/RAW_interactions.csv')
from_year, to_year = ('2008-01-01', '2017-12-31')
recipes['submitted'] = pd.to_datetime(recipes['sub... | code |
105206399/cell_42 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
def check(df):
l = []
columns = df.columns
for co... | code |
105206399/cell_9 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.describe() | code |
105206399/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.isnull().sum()
df_cars.duplicated().sum()
df_cars.columns
df_cars.drop('car_ID', axis=1, inplace=True)
df_cars.CarName.unique()
df... | code |
105206399/cell_57 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklear... | code |
105206399/cell_56 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklear... | code |
105206399/cell_34 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(... | code |
105206399/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
df_cars.isnull().sum()
df_cars.duplicated().sum()
df_cars.columns
df_cars.drop('car_ID', axis=1, inplace=True)
df_cars.CarName.unique()
df_cars.CarName.unique() | code |
105206399/cell_39 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
def check(df):
l = []
columns = df.columns
for co... | code |
105206399/cell_48 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklearn import linear_model
reg = linear... | code |
105206399/cell_61 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.c... | code |
105206399/cell_54 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
def check(df):
l = []
... | code |
105206399/cell_60 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import RobustScaler
ro_scaler = RobustScaler()
x_train = ro_scaler.fit_transform(x_train)
x_test = ro_scaler.fit_transform(x_test)
from sklear... | code |
105206399/cell_50 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.copy()
def check(df):
l = []
... | code |
105206399/cell_64 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import linear_model
from sklearn import linear_model
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df_cars = data.c... | code |
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