path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
323526/cell_9 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
323526/cell_11 | [
"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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
323526/cell_7 | [
"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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
323526/cell_16 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import roc_auc_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train... | code |
323526/cell_14 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.... | code |
323526/cell_12 | [
"text_plain_output_1.png",
"image_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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.... | code |
323526/cell_5 | [
"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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
129040249/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
data.head(5) | code |
129040249/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum() | code |
129040249/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.head(5) | code |
129040249/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data... | code |
129040249/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
JAPAN_data = data.sort_values(by=['J... | code |
129040249/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns | code |
129040249/cell_29 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
JAPAN_data = data.sort_values(by=['J... | code |
129040249/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape | code |
129040249/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
data.describe() | code |
129040249/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
data.info() | code |
129040249/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
JAPAN_data = data.sort_values(by=['J... | code |
129040249/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data.head(5) | code |
129040249/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates() | code |
129040249/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
data['Genre'].astype('category') | code |
129040249/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
Action_data = data[data['Genre'] == ... | code |
129040249/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
print('number of duplicate rows: ', duplicate_rows_data.shape... | code |
129040249/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes
data.isnull().sum()
duplicate_rows_data = data[data.duplicated()]
data.drop_duplicates()
data['Genre'].value_counts() | code |
129040249/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
data = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv', index_col='Rank')
data.shape
data.dtypes | code |
122258149/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isnull(df['FTHG'][j]) == True):
drop_index.append(j... | code |
122258149/cell_13 | [
"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/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isnull(df['FTHG'][j]) == True):
drop_index.append(j... | code |
122258149/cell_9 | [
"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/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isnull(df['FTHG'][j]) == True):
drop_index.append(j... | code |
122258149/cell_4 | [
"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/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
df.head() | code |
122258149/cell_30 | [
"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
df = pd.read_csv('/kaggle/input/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isn... | code |
122258149/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 |
122258149/cell_7 | [
"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/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isnull(df['FTHG'][j]) == True):
drop_index.append(j... | code |
122258149/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isnull(df['FTHG'][j]) == True):
drop_index.append(j... | code |
122258149/cell_24 | [
"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
df = pd.read_csv('/kaggle/input/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isn... | code |
122258149/cell_10 | [
"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)
df = pd.read_csv('/kaggle/input/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isnull(df['FTHG'][j]) == True):
drop_index.append(j... | code |
122258149/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/euro-football-data-since-2012/Euro-Football_2012-2023.csv')
drop_index = []
for j in range(0, len(df)):
if (pd.isnull(df['HomeTeam'][j]) == True) | (pd.isn... | code |
17142199/cell_13 | [
"text_plain_output_1.png"
] | from torch import tensor, nn
import torch.nn.functional as F
x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh)
class Model(nn.Module):
def __init__(self, ni, nh, no):
super().__init__()
self.layers = [nn.Linear(ni, nh), nn.ReLU(), nn.... | code |
17142199/cell_4 | [
"text_plain_output_1.png"
] | x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh) | code |
17142199/cell_6 | [
"text_plain_output_1.png"
] | from torch import tensor, nn
x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh)
class Model(nn.Module):
def __init__(self, ni, nh, no):
super().__init__()
self.layers = [nn.Linear(ni, nh), nn.ReLU(), nn.Linear(nh, no)]
def __call__(... | code |
17142199/cell_11 | [
"text_plain_output_1.png"
] | from torch import tensor, nn
import torch.nn.functional as F
x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh)
class Model(nn.Module):
def __init__(self, ni, nh, no):
super().__init__()
self.layers = [nn.Linear(ni, nh), nn.ReLU(), nn.... | code |
17142199/cell_7 | [
"text_plain_output_1.png"
] | from torch import tensor, nn
x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh)
class Model(nn.Module):
def __init__(self, ni, nh, no):
super().__init__()
self.layers = [nn.Linear(ni, nh), nn.ReLU(), nn.Linear(nh, no)]
def __call__(... | code |
17142199/cell_15 | [
"text_plain_output_1.png"
] | from torch import tensor, nn
import torch.nn.functional as F
x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh)
class Model(nn.Module):
def __init__(self, ni, nh, no):
super().__init__()
self.layers = [nn.Linear(ni, nh), nn.ReLU(), nn.... | code |
17142199/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import operator
from pathlib import Path
from IPython.core.debugger import set_trace
from fastai import datasets
import pickle, gzip, math, torch, matplotlib as mpl
import matplotlib.pyplot as plt
from torch import tensor, nn
import torch.nn... | code |
17142199/cell_14 | [
"text_plain_output_1.png"
] | from torch import tensor, nn
import torch.nn.functional as F
x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh)
class Model(nn.Module):
def __init__(self, ni, nh, no):
super().__init__()
self.layers = [nn.Linear(ni, nh), nn.ReLU(), nn.... | code |
17142199/cell_10 | [
"text_plain_output_1.png"
] | from torch import tensor, nn
import torch.nn.functional as F
x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh)
class Model(nn.Module):
def __init__(self, ni, nh, no):
super().__init__()
self.layers = [nn.Linear(ni, nh), nn.ReLU(), nn.... | code |
17142199/cell_5 | [
"text_plain_output_1.png"
] | from torch import tensor, nn
x_train, y_train, x_valid, y_valid = get_data()
n, m = x_train.shape
c = y_train.max() + 1
nh = 50
(n, m, c, nh)
class Model(nn.Module):
def __init__(self, ni, nh, no):
super().__init__()
self.layers = [nn.Linear(ni, nh), nn.ReLU(), nn.Linear(nh, no)]
def __call__(... | code |
50221349/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import lightgbm as lgb
import numpy as np # linear algebra
import optuna
import optuna
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn
train = pd.read_csv('/kaggle/input/jane-street-market-prediction/train.csv', nrows=3000... | code |
50221349/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import lightgbm as lgb
import optuna
from sklearn.model_selection import train_test_split
import sklearn | code |
50221349/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import lightgbm as lgb
import numpy as np # linear algebra
import optuna
import optuna
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn
train = pd.read_csv('/kaggle/input/jane-street-market-prediction/train.csv', nrows=3000... | code |
18159225/cell_13 | [
"text_html_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
import pandas as pd
import string
import numpy as np
import pandas as pd
import re
import nltk
import spacy
import string
pd.options.mode.chained_assignment = None
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df[... | code |
18159225/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import re
import nltk
import spacy
import string
pd.options.mode.chained_assignment = None
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df['text'] = df['text'].astype(str)
df['text_lower'] = df['text'].str.lower()
df.head... | code |
18159225/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import string
import numpy as np
import pandas as pd
import re
import nltk
import spacy
import string
pd.options.mode.chained_assignment = None
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df['text'] = df['text'].astype(str)
df['text_lower'] = df['text'].str.... | code |
18159225/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import re
import nltk
import spacy
import string
pd.options.mode.chained_assignment = None
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df['text'] = df['text'].astype(str)
full_df.head() | code |
18159225/cell_19 | [
"text_plain_output_1.png"
] | from nltk.stem.snowball import SnowballStemmer
from nltk.stem.snowball import SnowballStemmer
SnowballStemmer.languages | code |
18159225/cell_8 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.corpus import stopwords
', '.join(stopwords.words('english')) | code |
18159225/cell_15 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
import pandas as pd
import string
import numpy as np
import pandas as pd
import re
import nltk
import spacy
import string
pd.options.mode.chained_assignment = None
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df[... | code |
18159225/cell_17 | [
"text_html_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import pandas as pd
import string
import numpy as np
import pandas as pd
import re
import nltk
import spacy
import string
pd.options.mode.chained_assignment = None
full_df = pd.read_csv('../input/twcs/twcs.... | code |
18159225/cell_10 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import string
import numpy as np
import pandas as pd
import re
import nltk
import spacy
import string
pd.options.mode.chained_assignment = None
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df['text'] = df['text'].astype(str)
... | code |
18159225/cell_12 | [
"text_html_output_1.png"
] | from collections import Counter
from nltk.corpus import stopwords
import pandas as pd
import string
import numpy as np
import pandas as pd
import re
import nltk
import spacy
import string
pd.options.mode.chained_assignment = None
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df[... | code |
129008050/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
data
data = data.replace(np.nan, 0)
data
cor = data.corr()
data = data... | code |
129008050/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
data
df_c = data['car name'].astype('category')
data['car name'] = df_c.cat.codes
data['car name'] | code |
129008050/cell_6 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
data
data = data.replace(np.nan, 0)
data
plt.figure(figsize=(12, 10))
c... | code |
129008050/cell_2 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
data | code |
129008050/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.decomposition import PCA
from sklearn import linear_model
from keras.utils import to_categorical
im... | code |
129008050/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
data
data = data.replace(np.nan, 0)
data
cor = data.corr()
data = data... | code |
129008050/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
data
data = data.replace(np.nan, 0)
data
cor = data.corr()
data = data... | code |
129008050/cell_15 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
d... | code |
129008050/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
d... | code |
129008050/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
data
data = data.replace(np.nan, 0)
data
cor = data.corr()
data = data... | code |
129008050/cell_5 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv', na_values='?', comment='\t', skipinitialspace=True)
data
data = data.replace(np.nan, 0)
data | code |
73060893/cell_2 | [
"text_plain_output_1.png"
] | import warnings
import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
import pandas as pd
from sklearn import metrics
from sklearn.model_selection import train_test_split
import transformers
from transformers import AdamW, T5Tokenizer, T5ForCondit... | code |
73060893/cell_1 | [
"text_plain_output_1.png"
] | !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py
!python pytorch-xla-env-setup.py --version nightly --apt-packages libomp5 libopenblas-dev | code |
73060893/cell_3 | [
"application_vnd.jupyter.stderr_output_9.png",
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_8.png",
"text_plain_output_3.png",
"text_plain_o... | import transformers
class config:
MAX_LEN_I = 448
MAX_LEN_O = 224
TRAIN_BATCH_SIZE = 16
VALID_BATCH_SIZE = 8
EPOCHS = 15
MODEL_PATH = 'T5-base-TPU.pth'
TRAINING_FILE = '../input/table-to-text-generation-dataset-google-totto/totto_data/tablesWithTag.csv'
TOKENIZER = transformers.T5Tokeni... | code |
73060893/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset
from transformers import AdamW, T5Tokenizer, T5ForConditionalGeneration
import pandas as pd
import time
import torch
import torch_xla.core.xla_model as xm
special_tokens_dict = {'pad_token': '<pad>', 'bos_token': '<bos>', '... | code |
105187200/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)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
cats = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',... | code |
105187200/cell_23 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
data.groupby(['week_number'])['daily_conversion_rate '].sum().plot(figsize=... | code |
105187200/cell_30 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
cats = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',... | code |
105187200/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
fig, ax = plt.subplots(figsize=(13, 6))
ax... | code |
105187200/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
fig, ax = plt.subpl... | code |
105187200/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
data['week_number'] = pd.to_datetime(data['action_date']).dt.strftime('%U')... | code |
105187200/cell_41 | [
"application_vnd.jupyter.stderr_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
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
fig, ax = plt.subpl... | code |
105187200/cell_50 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
fig, ax = plt.subpl... | code |
105187200/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 |
105187200/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
data.head() | code |
105187200/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
cats = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',... | code |
105187200/cell_51 | [
"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
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
fig, ax = plt.subpl... | code |
105187200/cell_28 | [
"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)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
fig, ax = plt.subplots(figsize=(13,6))
# ... | code |
105187200/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)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape | code |
105187200/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.head() | code |
105187200/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.head() | code |
105187200/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/brainly/IC2 Data Analyst Taske home task - dataset for Part 2 - 2.1.csv')
data.shape
data.sort_values(by=['action_date'], inplace=True)
data['week_number'] = pd.to_datetime(data['action_date']).dt.strftime('%U')... | code |
33112913/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
33112913/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklea... | code |
33112913/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
33112913/cell_1 | [
"text_html_output_1.png",
"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 numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
import os
for dir... | code |
33112913/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_validate
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_... | code |
33112913/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
33112913/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
50227590/cell_7 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import BernoulliNB, MultinomialNB, GaussianNB
digits = datasets.load_digits()
breast = datasets.load_breast_cancer()
X_digits = digits.data
y_digits = digits.target
X_breast = breast.data
y_breast = breast.targ... | code |
73079642/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from keras.models import load_model
from tensorflow.keras import losses
import matplotlib.pyplot as plt
import numpy as np
def noise(a1, a2, channel):
"""
Adds random noise to each image in the sup... | code |
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