path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74056627/cell_26 | [
"text_plain_output_1.png"
] | df = pd.DataFrame({'a': np.random.choice(list('abcd')), 'b': np.random.rand(10000000)}) | code |
74056627/cell_11 | [
"text_plain_output_1.png"
] | !pip install line_profiler | code |
74056627/cell_7 | [
"text_plain_output_1.png"
] | for i in range(5):
pd.Series(np.random.randint(10, 20, 10000)) | code |
74056627/cell_18 | [
"text_plain_output_1.png"
] | code | |
74056627/cell_32 | [
"text_plain_output_1.png"
] | from numba import vectorize, int64
@vectorize([int64(int64)])
def vect_relu(n):
if n < 0:
return 0
else:
return n | code |
74056627/cell_15 | [
"text_plain_output_1.png"
] | total = 0
for val in s:
total += val | code |
74056627/cell_16 | [
"text_plain_output_1.png"
] | code | |
74056627/cell_17 | [
"text_plain_output_1.png"
] | code | |
74056627/cell_31 | [
"text_plain_output_1.png"
] | s = pd.Series(np.random.randint(-3, 10, 1000000))
def relu(n):
return 0 if n < 0 else n | code |
74056627/cell_24 | [
"text_plain_output_1.png"
] | code | |
74056627/cell_27 | [
"text_plain_output_1.png"
] | code | |
34126448/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
grid_df = pd.read_pickle('/kaggle/input/m5-simple-fe/grid_part_1.pkl')
grid_df = grid_df[['id', 'd', 'sales']].pivot(index='id', columns='d').reset_index()
ids = grid_df['id']
grid_df = grid_df['sales'].iloc[:... | code |
34126448/cell_6 | [
"text_plain_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)
import seaborn as sns
grid_df = pd.read_pickle('/kaggle/input/m5-simple-fe/grid_part_1.pkl')
grid_df = grid_df[['id', 'd', 'sales']].pivot(index='id', columns='d').reset_index(... | code |
34126448/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
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))
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
from scipy.cluster.hierarchy impor... | code |
34126448/cell_10 | [
"image_output_1.png"
] | from scipy.cluster.hierarchy import linkage, fcluster
from tqdm import tqdm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
grid_df = pd.read_pickle('/kaggle/input/m5-simple-fe/grid_part_1.pkl')
grid_df = grid_df[['id', 'd', 'sales']].pivot(index='id', col... | code |
18160674/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
18160674/cell_9 | [
"image_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
18160674/cell_6 | [
"image_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes | code |
18160674/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
18160674/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18160674/cell_7 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
data.show_batch(rows=3, figsize=(7, 8)) | code |
18160674/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
18160674/cell_15 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
18160674/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
18160674/cell_3 | [
"text_plain_output_1.png"
] | path = Path('../input/dataset')
path.ls() | code |
18160674/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
18160674/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
18160674/cell_12 | [
"image_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
path.ls()
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=False), size=256, num_workers=4).normalize(imagenet_stats)
data.classes
(data.classes, data.c, len(data.train_ds), len(dat... | code |
73068375/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_1 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, ... | code |
73068375/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_3 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, ... | code |
73068375/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
73068375/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings as filt
from scipy.stats import skew, norm
pd.options.display.max_columns =... | code |
88090498/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
sns.... | code |
88090498/cell_9 | [
"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 = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape | code |
88090498/cell_23 | [
"text_plain_output_1.png"
] | # installing openpyxl to run our excel file - pd.read_excel
!pip install openpyxl | code |
88090498/cell_6 | [
"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 = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.head() | code |
88090498/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
df_c... | code |
88090498/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
df_c... | code |
88090498/cell_54 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[fe... | code |
88090498/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 = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0] | code |
88090498/cell_52 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[fe... | code |
88090498/cell_7 | [
"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 = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns | code |
88090498/cell_49 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
df_c... | code |
88090498/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
df_c... | code |
88090498/cell_51 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[fe... | code |
88090498/cell_16 | [
"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 = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum() | code |
88090498/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[fe... | code |
88090498/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
df_c... | code |
88090498/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
df_c... | code |
88090498/cell_46 | [
"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)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
df_c... | code |
88090498/cell_24 | [
"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 = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df_country = pd.read_excel('../input/zomato-restaurants-data/Country-Code.xlsx')
df_country | code |
88090498/cell_10 | [
"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 = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.info() | code |
88090498/cell_27 | [
"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)
import seaborn as sns
df = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.isnull().sum()
[features for features in df.columns if df[features].isnull().sum() > 0]
df_c... | code |
88090498/cell_12 | [
"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 = pd.read_csv('../input/zomato-restaurants-data/zomato.csv', encoding='latin-1')
df.columns
df.shape
df.describe() | code |
322662/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import datetime
from subprocess import check_output
def dateparse(x):
try:
return pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
except TypeE... | code |
322662/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import datetime
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
def dateparse(x):
try:
print('Inside... | code |
322662/cell_3 | [
"text_plain_output_1.png"
] | """
def getDeltaTime(x):
r=(x[1] - x[0]).total_seconds()
return r
# It might make sense to add delta_s to the next version
d['delta_s']=d[['timeStamp0','timeStamp1']].apply(getDeltaTime, axis=1)
""" | code |
2000224/cell_4 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from subprocess import check_output
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, ... | code |
2000224/cell_6 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from subprocess import check_output
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, ... | code |
2000224/cell_2 | [
"text_plain_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from subprocess import check_output
import pandas as pd
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
from subproc... | code |
122253041/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
us_yt = pd.read_csv('../input/youtube-trending-video-dataset/US_youtube_trending_data.csv')
us_yt.categoryId.nunique()
us_yt[us_yt['view_count'].idxmax():us_yt['view_count'].idxmax() + 1] | code |
122253041/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
us_yt = pd.read_csv('../input/youtube-trending-video-dataset/US_youtube_trending_data.csv')
display(us_yt.head())
print(us_yt.columns) | code |
122253041/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
us_yt = pd.read_csv('../input/youtube-trending-video-dataset/US_youtube_trending_data.csv')
us_yt.categoryId.nunique()
us_yt.head() | code |
122253041/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
us_yt = pd.read_csv('../input/youtube-trending-video-dataset/US_youtube_trending_data.csv')
us_yt.categoryId.nunique()
corrolation_list = ['view_count', 'likes', 'dislikes', 'comment_count']
hm_data = us_yt[corrolation_list].corr()
display(hm_data) | code |
2009496/cell_2 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing,cross_validation,neighbors
import numpy as np # linear algebra
import pandas as pd
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, neighbors
def handle_non_numeric(df):
columns = df.columns.values
for col in columns:
text_d... | code |
2009496/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, neighbors
def handle_non_numeric(df):
columns = df.columns.values
for col in columns:
text_digit_vals = {}
def convert_to_int(val):
re... | code |
2009496/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import preprocessing,cross_validation,neighbors
import numpy as np # linear algebra
import pandas as pd
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, neighbors
def handle_non_numeric(df):
columns = df.columns.values
for col in columns:
text_d... | code |
332299/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
iris = pd.read_csv('../input/Iris.csv')
sns.jointplot(x='SepalLengthCm', y='SepalWidthCm', data=iris, size=5) | code |
332299/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
iris = pd.read_csv('../input/Iris.csv')
sns.FacetGrid(iris, hue='Species', size=5).map(plt.scatter... | code |
332299/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
iris = pd.read_csv('../input/Iris.csv')
iris['Species'].value_counts() | code |
332299/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
iris = pd.read_csv('../input/Iris.csv')
iris.head() | code |
332299/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
iris = pd.read_csv('../input/Iris.csv')
sns.FacetGrid(iris, hue='Species', size=5).map(plt.scatter... | code |
332299/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)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
iris = pd.read_csv('../input/Iris.csv')
iris.plot(kind='scatter', x='SepalLengthCm', y='SepalWidthCm') | code |
332299/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
iris = pd.read_csv('../input/Iris.csv')
sns.FacetGrid(iris, hue='Species', size=5).map(plt.scatter... | code |
130017103/cell_9 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | import pandas as pd
import warnings
train_clinical_all = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_clinical_data.csv')
proteins = pd.read_csv('/kaggle/input/amp-parkinsons-disease-progression-prediction/train_proteins.csv')
proteins_features = pd.pivot_table(proteins, values='NPX'... | code |
130017103/cell_19 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from scipy.optimize import minimize
from scipy.stats import mode
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import warnings
def smape_plus_1(y_true, y_pred):
y_true_plus_1 = y_true + 1
y_pred_plus_1 = y_pred + 1
metric = np.zeros(len(y_true_plus_1))
numerator = np.abs(y_true... | code |
130017103/cell_15 | [
"text_html_output_1.png"
] | from scipy.optimize import minimize
from scipy.stats import mode
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import warnings
def smape_plus_1(y_true, y_pred):
y_true_plus_1 = y_true + 1
y_pred_plus_1 = y_pred + 1
metric = np.zeros(len(y_true_plus_1))
numerator = np.abs(y_true... | code |
130017103/cell_17 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_3.png"
] | from scipy.optimize import minimize
from scipy.stats import mode
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import warnings
def smape_plus_1(y_true, y_pred):
y_true_plus_1 = y_true + 1
y_pred_plus_1 = y_pred + 1
metric = np.zeros(len(y_true_plus_1))
numerator = np.abs(y_true... | code |
128020295/cell_21 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020295/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020295/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
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