path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128015173/cell_13 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_9 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_25 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_34 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_33 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_44 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_40 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_29 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_39 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_26 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_48 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_19 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_49 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_32 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_28 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_8 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_38 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_3 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema') | code |
128015173/cell_35 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_43 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_27 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
128015173/cell_36 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
tokenizer = AutoTokenizer.from_pretrained('juierror/flan-t5-text2sql-with-schema')
model = AutoModelForSeq2SeqLM.from_pretrained('juierror/flan-t5-text2sql-with-schema')
def prepare_input(question: str, table: List[str]):
table... | code |
90137233/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import GaussianNB
train_y.value_counts()
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(stop_words='english')
train_x_vector = tfidf.fit_transform(train_x)
test_x_vector = tfidf.transform(test_x)... | code |
90137233/cell_30 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
import pandas as pd
df_review = pd.read_csv('/kaggle/input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
df_review
df_positive = df_review[df_review['sentiment'] == 'positive'][:9000]
df_negative = df_review[df_review['se... | code |
90137233/cell_20 | [
"text_html_output_1.png"
] | train_y.value_counts() | code |
90137233/cell_6 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90137233/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
df_review = pd.read_csv('/kaggle/input/imdb-dataset-of-50k-movi... | code |
90137233/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
train_y.value_counts()
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(stop_words='english')
train_x_vector = tfidf.fit_transform(train_x)
test_x_vector = tfidf.transform(test_x)
from sklearn.... | code |
90137233/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
df_review = pd.read_csv('/kaggle/input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
df_review
df_positive = df_review[df_review['sentiment'] == 'positive'][:9000]
df_negative = df_review[df_review['sentiment'] == 'negative'][:100... | code |
90137233/cell_50 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.model_selection import GridSearchCV
from sklearn.naive_ba... | code |
90137233/cell_45 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassif... | code |
90137233/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.tree import DecisionTreeClassifier
train_y.value_counts()
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(stop_words='english')
train_x_vector = tfidf.fit_transform(train_x)
test_x_vector = tfidf.transform(te... | code |
90137233/cell_51 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.model_selection import GridSearchCV
from sklearn.naive_ba... | code |
90137233/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df_review = pd.read_csv('/kaggle/input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
df_review | code |
90137233/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
df_review = pd.read_csv('/kaggle/input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
df_review
df_positive = df_rev... | code |
90137233/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC... | code |
90137233/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
df_review = pd.read_csv('... | code |
90137233/cell_14 | [
"text_html_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
df_review = pd.read_csv('/kaggle/input/imdb-dataset-of-50k-movie-re... | code |
90137233/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
df_review = pd.read_csv('/kaggle/input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
df_review
df_positive = df_rev... | code |
90137233/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
train_y.value_counts()
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(stop_words='english')
train_x_vector = tfidf.fit_trans... | code |
33105010/cell_13 | [
"image_output_1.png"
] | from tensorflow.keras import Model
from tensorflow.keras.layers import Input, Dense
import matplotlib.pyplot as plt
import tensorflow as tf
def sample_dataset():
dataset_shape = (2000, 1)
return tf.random.normal(mean=8.0, shape=dataset_shape, stddev=0.5, dtype=tf.float32)
axes = plt.gca()
axes.set_xlim([-1,... | code |
33105010/cell_4 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_out... | import matplotlib.pyplot as plt
import tensorflow as tf
def sample_dataset():
dataset_shape = (2000, 1)
return tf.random.normal(mean=8.0, shape=dataset_shape, stddev=0.5, dtype=tf.float32)
plt.hist(sample_dataset().numpy(), 100)
axes = plt.gca()
axes.set_xlim([-1, 11])
axes.set_ylim([0, 70])
plt.show() | code |
130004323/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score, mea... | code |
130004323/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
plt.figure(figsize=(10, 8))
sns.heatmap(data.corr(), cmap='RdBu')
plt.title('Correlations Between Variables', size=15)
plt.show() | code |
130004323/cell_12 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split, cross_val_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('..... | code |
130004323/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
important = list(data.corr()['SalePrice'][(data.corr()['SalePrice'] > 0.5) | (data.corr()['SalePrice'] < -0.5)].index)
cat_columns = ['MSZoning', 'Utilities'... | code |
18102746/cell_21 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_9.png",
"image_output_14.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_outp... | 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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_D... | code |
18102746/cell_13 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.Series.to_frame(Matches_Data['Sta... | code |
18102746/cell_9 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.Series.to_frame(Matches_Data['Sta... | code |
18102746/cell_25 | [
"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 numpy as np
import pandas as pd
import seaborn as sns
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.... | code |
18102746/cell_23 | [
"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 numpy as np
import pandas as pd
import seaborn as sns
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.... | code |
18102746/cell_30 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_D... | code |
18102746/cell_20 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_D... | code |
18102746/cell_6 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.Series.to_frame(Matches_Data['Sta... | code |
18102746/cell_29 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.p... | 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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_P... | code |
18102746/cell_11 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.Series.to_frame(Matches_Data['Sta... | code |
18102746/cell_7 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.Series.to_frame(Matches_Data['Sta... | code |
18102746/cell_18 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_D... | code |
18102746/cell_28 | [
"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)
import numpy as np
import pandas as pd
import seaborn as sns
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_D... | code |
18102746/cell_8 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.Series.to_frame(Matches_Data['Sta... | code |
18102746/cell_15 | [
"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 numpy as np
import pandas as pd
import seaborn as sns
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.... | code |
18102746/cell_16 | [
"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 numpy as np
import pandas as pd
import seaborn as sns
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.... | code |
18102746/cell_17 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_Da... | code |
18102746/cell_31 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_D... | code |
18102746/cell_24 | [
"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 numpy as np
import pandas as pd
import seaborn as sns
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.... | code |
18102746/cell_22 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_D... | code |
18102746/cell_27 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Deliveries_Data.head(2)
Deliveries_Powerplay = Deliveries_D... | code |
18102746/cell_5 | [
"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
Deliveries_Data = pd.read_csv('../input/deliveries_expanded.csv')
Matches_Data = pd.read_csv('../input/matches_expanded.csv')
Overall_Matches_State = pd.Series.to_frame(Matches_Data['Sta... | code |
121153872/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)[(t... | code |
121153872/cell_9 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.info() | code |
121153872/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import seaborn as sns
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]... | code |
121153872/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import seaborn as sns
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]... | code |
121153872/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.shape | code |
121153872/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import seaborn as sns
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]... | code |
121153872/cell_19 | [
"image_output_1.png"
] | import matplotlib.style as style
import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]
percent = (train.isnull().sum() / train.isnull().count(... | code |
121153872/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.head() | code |
121153872/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import seaborn as sns
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]... | code |
121153872/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import seaborn as sns
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]... | code |
121153872/cell_8 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.describe().T | code |
121153872/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.style as style
import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]
percent = (train.isnull().sum() / train.isnull().count(... | code |
121153872/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import seaborn as sns
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]... | code |
121153872/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.style as style
import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
total = train.isnull().sum().sort_values(ascending=False)[train.isnull().sum().sort_values(ascending=False) != 0]
percent = (train.isnull().sum() / train.isnull().count(... | code |
121153872/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts() | code |
121153872/cell_12 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd
train = pd.read_csv('train.csv')
train.shape
train.describe().T
train.dtypes.value_counts()
msno.matrix(train) | code |
17108148/cell_13 | [
"image_output_1.png"
] | import pandas as pd # data , CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
structures = pd.read_csv('../input/structures.csv')
molecule = 'dsgdb9nsd_000001'
a = df_train.loc[df_train['molecule_name'] == f'{molecule}']
b = structures[structures.molecule_name == f'{molecule}']
de... | code |
17108148/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data , CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
structures = pd.read_csv('../input/structures.csv')
molecule = 'dsgdb9nsd_000001'
a = df_train.loc[df_train['molecule_name'] == f'{molecule}']
b = structures[structures.molecule_name == f'{molecule}']
li... | code |
17108148/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data , CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
structures = pd.read_csv('../input/structures.csv')
molecule = 'dsgdb9nsd_000001'
a = df_train.loc[df_train['molecule_name'] == f'{molecule}']
b = structures[structures.molecule_name == f'{molecule}']
de... | code |
17108148/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
print(os.listdir('../input')) | code |
17108148/cell_14 | [
"image_output_1.png"
] | import pandas as pd # data , CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
structures = pd.read_csv('../input/structures.csv')
molecule = 'dsgdb9nsd_000001'
a = df_train.loc[df_train['molecule_name'] == f'{molecule}']
b = structures[structures.molecule_name == f'{molecule}']
de... | code |
17108148/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data , CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
structures = pd.read_csv('../input/structures.csv')
molecule = 'dsgdb9nsd_000001'
a = df_train.loc[df_train['molecule_name'] == f'{molecule}']
b = structures[structures.molecule_name == f'{molecule}']
de... | code |
17108148/cell_12 | [
"image_output_1.png"
] | import pandas as pd # data , CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
structures = pd.read_csv('../input/structures.csv')
molecule = 'dsgdb9nsd_000001'
a = df_train.loc[df_train['molecule_name'] == f'{molecule}']
b = structures[structures.molecule_name == f'{molecule}']
de... | code |
88092182/cell_21 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.preprocessing import OneHotEncoder
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-feb-... | code |
88092182/cell_13 | [
"text_plain_output_1.png"
] | y_train.shape | code |
88092182/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/train.csv', index_col=0)
test = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/test.csv', index_col=0)
submission = pd.read_csv('/kaggle/input/tabular-playground-s... | code |
88092182/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.preprocessing import OneHotEncoder
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-feb-... | code |
88092182/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))
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection im... | code |
88092182/cell_8 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/train.csv', index_col=0)
test = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/test.csv', index_col=0)
submission ... | code |
88092182/cell_15 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/train.csv', index_col=0)
test = pd.read... | code |
88092182/cell_16 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.preprocessing import OneHotEncoder
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-feb-... | code |
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