path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72085616/cell_53 | [
"text_plain_output_1.png"
] | X_valid_full.shape
X_valid_full.columns | code |
72085616/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
y = data.Price
y.isnull().count()
melb_predictors = data.drop(['Price'], axis=1)
melb_predictors.shape
melb_predictors.dtypes
X = melb_pred... | code |
72085616/cell_71 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
72085616/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
y = data.Price
y.isnull().count() | code |
72085616/cell_36 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
data.shape
pd.set_option('display.max_columns', None)
cols_with_missing = [col for col in X_train.columns if X... | code |
322985/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass... | code |
322985/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.loc[full_data_set.PlayType == 'Pass... | code |
322985/cell_5 | [
"text_html_output_1.png",
"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)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_se... | code |
331783/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_indicators[df_indicators.CountryName == 'Indonesia'].drop_duplicates('IndicatorCode')['Indi... | code |
331783/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)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_indicators[df_indicators.CountryName == 'Indonesia'].drop_duplicates('IndicatorCode')['Indi... | code |
331783/cell_6 | [
"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)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_indicators[df_indicators.CountryName == 'Indonesia'].drop_... | code |
331783/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
331783/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_indicators[df_indicators.CountryName == 'Indonesia'].drop_duplicates('IndicatorCode')['Indi... | code |
331783/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indicators.csv')
df_series = pd.read_csv('../input/Series.csv')
df_countries = pd.read_csv('../input/Country.csv')
df_indicators = pd.read_csv('../input/Indic... | code |
129018594/cell_9 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from sentence_transformers import InputExample
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator
from torch.utils.data import DataLoader
dataset_train = load_dataset('stsb_multi_mt', name=... | code |
129018594/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datasets import load_dataset
dataset_train = load_dataset('stsb_multi_mt', name='it', split='train')
dataset_test = load_dataset('stsb_multi_mt', name='it', split='test') | code |
129018594/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install datasets | code |
129018594/cell_1 | [
"text_plain_output_1.png"
] | !pip install -U sentence-transformers | code |
129018594/cell_10 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from sentence_transformers import InputExample
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator
from torch.utils.data import DataLoader
dataset_train = load_dataset('stsb_multi_mt', name=... | code |
18159419/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_date', 'daysold', 'topics', 'coauthors', 'linkedinphoto', ... | code |
18159419/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_date', 'daysold', 'topics', 'coauthors', 'linkedinphoto', ... | code |
18159419/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
data.head() | code |
18159419/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
(train_df.shape, val_df.shape)
accuracy_score(train_df['Reg_clicks_categorical'], train_df['prediction']) | code |
18159419/cell_23 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_date', 'da... | code |
18159419/cell_30 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Input, CuDNNLSTM, Embedding, Dropout, Activation, CuDNNGRU, Conv1D
from keras.layers import LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical
import p... | code |
18159419/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
(train_df.shape, val_df.shape)
confusion_matrix(train_df['Reg_clicks_categorical'], train_df['prediction']) | code |
18159419/cell_20 | [
"text_plain_output_1.png"
] | (train_df.shape, val_df.shape) | code |
18159419/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_date', 'daysold', 'topics', 'coauthors', 'linkedinphoto', ... | code |
18159419/cell_29 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Input, CuDNNLSTM, Embedding, Dropout, Activation, CuDNNGRU, Conv1D
from keras.layers import LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical
import p... | code |
18159419/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_date', 'daysold', 'topics', 'coauthors', 'linkedinphoto', ... | code |
18159419/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from textblob import TextBlob
import nltk
import re
from bs4 import BeautifulSoup
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from sklearn.feature_extraction.text import TfidfVectorizer
from... | code |
18159419/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_da... | code |
18159419/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_date', 'daysold', 'topics', 'coauthors', 'linkedinphoto', ... | code |
18159419/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_da... | code |
18159419/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_date', 'daysold', 'topics', 'coauthors', 'linkedinphoto', ... | code |
18159419/cell_14 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mondaq_data_adeel.csv', header=None)
columns = ['article_id', 'knt', 'unknt', 'her_knt', 'unher_knt', 'company_id', 'company_name', 'country_id', 'country_desc', 'primary_topic_id', 'topic_desc', 'article_start_date', 'daysold', 'topics', 'coauthors', 'linkedinphoto', ... | code |
18159419/cell_22 | [
"image_output_1.png"
] | (train_df.shape, val_df.shape)
train_X = train_df['entire_text'].fillna('##').values
val_X = val_df['entire_text'].fillna('##').values
print('before tokenization')
print(train_X.shape)
print(val_X.shape) | code |
18159419/cell_27 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Input, CuDNNLSTM, Embedding, Dropout, Activation, CuDNNGRU, Conv1D
from keras.layers import LSTM
from keras.models import Sequential
embed_size = 100
max_features = 100000
maxlen = 1400
model = Sequential()
model.add(Embedding(max_features, embed_size, input_length=maxlen))
model.add... | code |
17132918/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import random
import random
import matplotlib
import matplotlib.pyplot as plt
def throw():
global x
x += random.randint(1, 6)
def multi_throw(dice_amount, list_name):
global x
x = 0
for i in range(dice_amount):
throw()
list_name.append(x)
def multi_times... | code |
17132918/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import random
import random
import matplotlib
import matplotlib.pyplot as plt
def throw():
global x
x += random.randint(1, 6)
def multi_throw(dice_amount, list_name):
global x
x = 0
for i in range(dice_amount):
throw()
list_name.append(x)
def multi_times... | code |
17132918/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import random
import random
import matplotlib
import matplotlib.pyplot as plt
def throw():
global x
x += random.randint(1, 6)
def multi_throw(dice_amount, list_name):
global x
x = 0
for i in range(dice_amount):
throw()
list_name.append(x)
def multi_times... | code |
17132918/cell_15 | [
"image_output_1.png"
] | import random
import seaborn as sns
import random
import matplotlib
import matplotlib.pyplot as plt
def throw():
global x
x += random.randint(1, 6)
def multi_throw(dice_amount, list_name):
global x
x = 0
for i in range(dice_amount):
throw()
list_name.append(x)
def multi_times(time_amou... | code |
17132918/cell_16 | [
"image_output_1.png"
] | import random
import seaborn as sns
import random
import matplotlib
import matplotlib.pyplot as plt
def throw():
global x
x += random.randint(1, 6)
def multi_throw(dice_amount, list_name):
global x
x = 0
for i in range(dice_amount):
throw()
list_name.append(x)
def multi_times(time_amou... | code |
17132918/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import random
import seaborn as sns
import random
import matplotlib
import matplotlib.pyplot as plt
def throw():
global x
x += random.randint(1, 6)
def multi_throw(dice_amount, list_name):
global x
x = 0
for i in range(dice_amount):
throw()
list_name.ap... | code |
17132918/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import random
import random
import matplotlib
import matplotlib.pyplot as plt
def throw():
global x
x += random.randint(1, 6)
def multi_throw(dice_amount, list_name):
global x
x = 0
for i in range(dice_amount):
throw()
list_name.append(x)
def multi_times... | code |
2020321/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtype... | code |
2020321/cell_9 | [
"text_html_output_1.png"
] | train.groupby('Utilities')['SalePrice'].agg(['median', 'mean', 'std']) | code |
2020321/cell_4 | [
"text_plain_output_1.png"
] | categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtypes[entire.dtypes == 'int64'].index))
print('{} categorical features:\n'.format(len(categ_feat)), categ_feat)... | code |
2020321/cell_19 | [
"text_html_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtype... | code |
2020321/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtypes[entire.dtypes == 'int64'].ind... | code |
2020321/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtypes[entire.dtypes == 'int64'].ind... | code |
2020321/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | print('Total number of rows: {0}\n\t- {1} in training set\n\t- {2} in testing set\nNumber of features: {3}'.format(len(entire.index), len(train.index), len(test.index), len(train.columns))) | code |
2020321/cell_17 | [
"text_plain_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtype... | code |
2020321/cell_14 | [
"image_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtype... | code |
2020321/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtypes[entire.dtypes == 'int64'].ind... | code |
2020321/cell_12 | [
"text_plain_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
categ_feat = sorted(list(entire.dtypes[entire.dtypes == 'object'].index))
num_feat = sorted(list(entire.dtypes[(entire.dtypes == 'int64') | (entire.dtypes == 'float64')].index))
int_feat = sorted(list(entire.dtype... | code |
48165213/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
top_10_publishers = games.Publisher.value_counts().sort_values(ascending=False).head(10)
fig = px.pie(top_10_publishers,
values= top_10_publishers.values,
... | code |
48165213/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
top_10_publishers = games.Publisher.value_counts().sort_values(ascending=False).head(10)
fig = px.pie(top_10_publishers,
values= top_10_publishers.values,
... | code |
48165213/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from IPython.display import display
import seaborn as sns
import plotly
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objs as go
plotly.offline.init_notebook_mode(connected=True)
from plotly.subplots import make_subplots
import os
for dirname, _, ... | code |
48165213/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
top_10_publishers = games.Publisher.value_counts().sort_values(ascending=False).head(10)
fig = px.pie(top_10_publishers, values=top_10_publishers.values, names=top_10_publishers.index, title='Top 10 Games ... | code |
48165213/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
top_10_publishers = games.Publisher.value_counts().sort_values(ascending=False).head(10)
fig = px.pie(top_10_publishers,
values= top_10_publishers.values,
... | code |
48165213/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
top_10_publishers = games.Publisher.value_counts().sort_values(ascending=False).head(10)
fig = px.pie(top_10_publishers,
values= top_10_publishers.values,
... | code |
48165213/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
top_10_publishers = games.Publisher.value_counts().sort_values(ascending=False).head(10)
fig = px.pie(top_10_publishers,
values= top_10_publishers.values,
... | code |
48165213/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
games | code |
48165213/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
top_10_publishers = games.Publisher.value_counts().sort_values(ascending=False).head(10)
fig = px.pie(top_10_publishers,
values= top_10_publishers.values,
... | code |
48165213/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
top_10_publishers = games.Publisher.value_counts().sort_values(ascending=False).head(10)
fig = px.pie(top_10_publishers,
values= top_10_publishers.values,
... | code |
48165213/cell_5 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
games = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
sns.set_style('whitegrid')
fig, ax1 = plt.subplots(figsize=(20, 11))
plt.title('Number of different games per game platforms')
sns.countplot(x='Platform', data=games, ax=ax1)
plt... | code |
329717/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
print(df.head()) | code |
128026337/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
def knight_moves(pos):
x, y = pos
moves = [(x + 1, y + 2), (x + 1, y - 2), (x - 1, y + 2), (x - 1, y - 2), (x + 2, y + 1), (x + 2, y - 1), (x - 2, y + 1), (x - 2, y - 1)]
return [(a, b) for a, b in moves if 0 <= a < 8 and 0 <= b < 8]
def create_chessb... | code |
128026337/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
def knight_moves(pos):
x, y = pos
moves = [(x + 1, y + 2), (x + 1, y - 2), (x - 1, y + 2), (x - 1, y - 2), (x + 2, y + 1), (x + 2, y - 1), (x - 2, y + 1), (x - 2, y - 1)]
return [(a, b) for a, b in moves if 0 <= a < 8 and 0 <= b < 8]
def create_chessb... | code |
128026337/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
def knight_moves(pos):
x, y = pos
moves = [(x + 1, y + 2), (x + 1, y - 2), (x - 1, y + 2), (x - 1, y - 2), (x + 2, y + 1), (x + 2, y - 1), (x - 2, y + 1), (x - 2, y - 1)]
return [(a, b) for a, b in moves if 0 <= a < 8 and 0 <= b < 8]
def create_chessb... | code |
128026337/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
def knight_moves(pos):
x, y = pos
moves = [(x + 1, y + 2), (x + 1, y - 2), (x - 1, y + 2), (x - 1, y - 2), (x + 2, y + 1), (x + 2, y - 1), (x - 2, y + 1), (x - 2, y - 1)]
return [(a, b) for a, b in moves if 0 <= a < 8 and 0 <= b < 8]
def create_chessb... | code |
128027620/cell_21 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
def get_length_of_t... | code |
128027620/cell_13 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
def get_length_of_text(x):
return len(... | code |
128027620/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
train.phraseology.hist() | code |
128027620/cell_25 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
def get_length_of_t... | code |
128027620/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-l... | code |
128027620/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
train.cohesion.hist() | code |
128027620/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from tqdm import tqdm
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.... | code |
128027620/cell_11 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
train.conventions.hist() | code |
128027620/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
train.syntax.hist() | code |
128027620/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
train.vocabulary.hist() | code |
128027620/cell_16 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
def get_length_of_text(x):
return len(... | code |
128027620/cell_3 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
train.head() | code |
128027620/cell_17 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
def get_length_of_t... | code |
128027620/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
train.grammar.hist() | code |
128027620/cell_12 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
def get_length_of_text(x):
return len(... | code |
129035281/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/performance-data-on-football-teams-09-to-22/Complete Dataset 2.csv')
df.describe() | code |
129035281/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
import pingouin as pg
df = pd.read_csv('/kaggle/input/performance-data-on-football-teams-09-to-22/Complete Dataset 2.csv')
df.dtypes
pl = df[(df['League'] == 'Premier League') & (df['Rank'] == 1)][['League', 'Team', 'Start Season', 'End Season', 'Points', 'Wins']].reset_index(drop=True)
ll = df... | code |
129035281/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/performance-data-on-football-teams-09-to-22/Complete Dataset 2.csv')
df.dtypes
df.info() | code |
129035281/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/performance-data-on-football-teams-09-to-22/Complete Dataset 2.csv')
df.tail() | code |
129035281/cell_40 | [
"text_html_output_1.png"
] | import pandas as pd
import pingouin as pg
df = pd.read_csv('/kaggle/input/performance-data-on-football-teams-09-to-22/Complete Dataset 2.csv')
df.dtypes
pl = df[(df['League'] == 'Premier League') & (df['Rank'] == 1)][['League', 'Team', 'Start Season', 'End Season', 'Points', 'Wins']].reset_index(drop=True)
ll = df... | code |
129035281/cell_11 | [
"text_html_output_1.png"
] | !wget http://bit.ly/3ZLyF82 -O CSS.css -q
from IPython.core.display import HTML
with open('./CSS.css', 'r') as file:
custom_css = file.read()
HTML(custom_css) | code |
129035281/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/performance-data-on-football-teams-09-to-22/Complete Dataset 2.csv')
df.head() | code |
129035281/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pingouin as pg | code |
129035281/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/performance-data-on-football-teams-09-to-22/Complete Dataset 2.csv')
df.dtypes
pl = df[(df['League'] == 'Premier League') & (df['Rank'] == 1)][['League', 'Team', 'Start Season', 'End Season', 'Points', 'Wins']].reset_index(drop=True)
ll = df[(df['League'] == 'La L... | code |
129035281/cell_14 | [
"text_plain_output_1.png"
] | ! pip install pingouin | code |
129035281/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/performance-data-on-football-teams-09-to-22/Complete Dataset 2.csv')
df.dtypes | code |
49124084/cell_4 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/twitter-train/train.txt', delimiter='\n', header=None)
data_array = data.to_numpy()
x_array = np.reshape(data_array, (-1, 3))
print(x_array)... | code |
49124084/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/twitter-train/train.txt', delimiter='\n', header=None)
data_array = data.to_numpy()
x_array = np.reshape(data_array, (-1, 3))
column = ['Twe... | code |
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