path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72100024/cell_10 | [
"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/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(co... | code |
72100024/cell_12 | [
"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/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(co... | code |
32062611/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.ticker import MaxNLocator
from sklearn.mixture import GaussianMixture
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Iris.csv')
data
features = ['PetalLengthCm', 'PetalWidthCm']
color_dict = {'Iris-setosa': 'darkred', 'Ir... | code |
32062611/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/Iris.csv')
data
features = ['PetalLengthCm', 'PetalWidthCm']
color_dict = {'Iris-setosa': 'darkred', 'Iris-versicolor': 'Yellow', 'Iris-virginica': 'Green'}
names_dict = {'Iris-setosa': 'Setosa', 'Iris-versicolor': 'Versicolor', 'Iris-... | code |
32062611/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Iris.csv')
data | code |
32062611/cell_11 | [
"text_html_output_1.png"
] | from matplotlib.ticker import MaxNLocator
from sklearn.mixture import GaussianMixture
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Iris.csv')
data
features = ['PetalLengthCm', 'PetalWidthCm']
color_dict = {'Iris-setosa': 'darkred', 'Ir... | code |
32062611/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.ticker import MaxNLocator
from sklearn.mixture import GaussianMixture
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Iris.csv')
data
features = ['PetalLengthCm', 'PetalWidthCm']
color_dict = {'Iris-setosa': 'darkred', 'Ir... | code |
32062611/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.ticker import MaxNLocator
from sklearn.mixture import GaussianMixture
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Iris.csv')
data
features = ['PetalLengthCm', 'PetalWidthCm']
color_dict = {'Iris-setosa': 'darkred', 'Ir... | code |
32062611/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.ticker import MaxNLocator
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/Iris.csv')
data
features = ['PetalLengthCm', 'PetalWidthCm']
color_dict = {'Iris-setosa': 'darkred', 'Iris-versicolor': 'Yellow', 'Iris-virginica': 'Green'}
names_dict = {'... | code |
1007568/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
train = pd.read_csv('.... | code |
1007568/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
train = pd.read_csv('../input/train.csv')
... | code |
1007568/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
train = pd.read_csv('.... | code |
1007568/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
train = pd.read_csv('.... | code |
1007568/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
train = pd.read_csv... | code |
1007568/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB... | code |
1007568/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
train = pd.read_csv('../input/train.csv')
... | code |
1007568/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
train = pd.read_csv('.... | code |
1007568/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
train = pd.read_csv('.... | code |
88082992/cell_11 | [
"text_plain_output_1.png"
] | from ast import literal_eval
from pathlib import Path
from tqdm.notebook import tqdm
import joblib
import json
import numpy as np
import pandas as pd
import pytorch_lightning as pl
SEED = 42
ROOT_DIR = '../input'
MEL_PATHS = sorted(Path(ROOT_DIR).glob('birdclef-2022-melspectrogram-compute/rich_train_metadata.cs... | code |
88082992/cell_15 | [
"text_plain_output_1.png"
] | from ast import literal_eval
from pathlib import Path
from torch.utils.data import Dataset, DataLoader
from tqdm.notebook import tqdm
import joblib
import json
import numpy as np
import pandas as pd
import pytorch_lightning as pl
SEED = 42
ROOT_DIR = '../input'
MEL_PATHS = sorted(Path(ROOT_DIR).glob('birdclef-... | code |
88082992/cell_17 | [
"text_plain_output_1.png"
] | from ast import literal_eval
from pathlib import Path
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
from torchmetrics import F1
from tqdm.notebook import tqdm
import joblib
import json
import numpy as np
im... | code |
121149840/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline... | code |
121149840/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
sns.regplot(x='sqft_above', y='price', data=df)
plt.title('Price vs. sqft_above')
plt.xlabel('sqf... | code |
121149840/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_spli... | code |
121149840/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
print(df.dtypes) | code |
121149840/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
counts = df['floors'].value_counts().to_frame()
print(counts) | code |
121149840/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
from sklearn.pipeline import Pipeline
import pandas as pd
import pandas as pd # data proc... | code |
121149840/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
sns.boxplot(x='waterfront', y='price', data=df)
plt.title('Price distribution by Waterfront View'... | code |
121149840/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)... | code |
121149840/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_spli... | code |
121149840/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
df.corr()['pr... | code |
121149840/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
df.corr()['price'].sort_values() | code |
121149840/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
df.corr()['price'].sort_values()
X = df[['long']]
Y = df['price']
lm = LinearRegression()
lm.fit(X, Y... | code |
121149840/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
df.head() | code |
74060570/cell_42 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')... | code |
74060570/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
prep = GetDfForPreprocessing(produc... | code |
74060570/cell_57 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
produc... | code |
74060570/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
prep = GetDfForPreprocessing(produc... | code |
74060570/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
products_df = products_df[products_... | code |
74060570/cell_55 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
prep = GetDfForPreprocessing(produc... | code |
74060570/cell_65 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_61 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_54 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
products_df = products_df[products_... | code |
74060570/cell_67 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_df.head() | code |
74060570/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_df = districts_df[distric... | code |
74060570/cell_64 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')... | code |
74060570/cell_45 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_49 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_df = districts_df[distric... | code |
74060570/cell_51 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_59 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
products_df = products_df[products_... | code |
74060570/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
prep_dist = GetDfForPreprocessing(d... | code |
74060570/cell_47 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
districts_df = districts_df[distric... | code |
74060570/cell_35 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')... | code |
74060570/cell_43 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatfor... | code |
74060570/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
prep_dist = GetDfForPreprocessing(d... | code |
74060570/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
products_df.head() | code |
74060570/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')
prep_dist = GetDfForPreprocessing(d... | code |
74060570/cell_36 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
districts_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/districts_info.csv')
products_df = pd.read_csv('../input/learnplatform-covid19-impact-on-digital-learning/products_info.csv')... | code |
128034186/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
train.head() | code |
128034186/cell_4 | [
"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/spaceship-titanic/train.csv')
train.head() | code |
128034186/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)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
def split_col(df, col_list, delimiter_list):
new_df = pd.DataFrame()
for i, col in enumerate(col_list):
new_cols = df[col].str... | code |
128034186/cell_6 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
msno.matrix(train) | code |
128034186/cell_26 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelBinarizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
def split_col(df, col_list, delimiter_list):
new_df = pd.DataFrame()
for i, col in e... | code |
128034186/cell_11 | [
"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
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
fig, ax = plt.subplots(1, 2)
fig.set_figheight(5)
fig.set_figwidth(12)
sns.countplot(t... | code |
128034186/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
def split_col(df, col_list, delimiter_list):
new_df = pd.DataFrame()
for i, col in enumerate(col_list):
new_cols = df[col].str... | code |
128034186/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import missingno as msno
from sklearn.preprocessing import label_binarize
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import KFold, cross_val_score
from sklearn.ensemble import Rando... | code |
128034186/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('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum() | code |
128034186/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
def split_col(df, col_list, delimiter_list):
new_df = pd.DataFrame()
for i, col in enumerate(col_list):
new_cols = df[col].str... | code |
128034186/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.info() | code |
128034186/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
def split_col(df, col_list, delimiter_list):
new_df = pd.DataFrame()
for i, col in enumerate(col_list):
new_cols = df[col].str... | code |
128034186/cell_31 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold, cross_val_score
from sklearn.preprocessing import LabelBinarizer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_... | code |
128034186/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
def split_col(df, col_list, delimiter_list):
new_df = pd.DataFrame()
for i, col in enumerate(col_list):
new_cols = df[col].str... | code |
128034186/cell_22 | [
"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/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
def split_col(df, col_list, delimiter_list):
new_df = pd.DataFrame()
for i, col in enumerate(col_list):
new_cols = df[col].str... | code |
128034186/cell_10 | [
"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/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum() | code |
128034186/cell_12 | [
"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
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
train.isna().sum()
train.isna().sum()
fig, ax = plt.subplots(1, 2)
fig.set_figheight(5)
fig.set_figwidth(12)
sns.countplot(t... | code |
128034186/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('/kaggle/input/spaceship-titanic/train.csv')
for col in train:
print('===> {}, unique values: {}'.format(col, train[col].nunique()))
print(train[col].unique())
print('\n') | code |
128011575/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_all = pd.read_csv('/kaggle/input/imdb-dataset-of-top-1000-movies-and-tv-shows/imdb_top_1000.csv')
imdb_all['Gross'] = imdb_all['Gross'].str.replace(',', '')
imdb_all['Gross'] = pd.to_numeric(imdb_all['Gross'])
imdb_all['Gross'] = imdb_all['Gr... | code |
128011575/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
imdb_all = pd.read_csv('/kaggle/input/imdb-dataset-of-top-1000-movies-and-tv-shows/imdb_top_1000.csv')
imdb_all.head() | code |
128011575/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 |
128011575/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
imdb_all = pd.read_csv('/kaggle/input/imdb-dataset-of-top-1000-movies-and-tv-shows/imdb_top_1000.csv')
imdb_all['Gross'] = imdb_all['Gross'].str.replace('... | code |
128011575/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.palplot(['#DBA506', '#F2DB83', '#000000'])
plt.title('IMDB brand pallete', loc='left', fontfamily='serif', fontsize=15, y=1.2)
plt.show() | code |
104130523/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
val_data = all_train_data[:int(0.2 * len(all_tra... | code |
104130523/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
val_data = all_train_data[:int(0.2 * len(all_train_data))]
train_data = all_train_data[int(0.2 * len(all_train_data)):].reset_index()
test_data = p... | code |
104130523/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 |
104130523/cell_3 | [
"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)
all_train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
val_data = all_train_data[:int(0.2 * len(all_train_data))]
train_data = all_train_data[int(0.2 * len(all_train_data)):].reset_index()
test_data = pd.read_csv('/kaggle/input/titanic/tes... | code |
128016055/cell_13 | [
"text_plain_output_1.png"
] | from blip.models import blip
import importlib
import inspect
blip_path = inspect.getfile(blip)
fin = open(blip_path, 'rt')
data = fin.read()
data = data.replace("BertTokenizer.from_pretrained('bert-base-uncased')", "BertTokenizer.from_pretrained('/kaggle/input/clip-interrogator-models-x/bert-base-uncased')")
fin.clo... | code |
128016055/cell_9 | [
"text_plain_output_1.png"
] | !pip install --no-index --find-links $wheels_path $clip_interrogator_whl_path -q | code |
128016055/cell_34 | [
"text_plain_output_1.png"
] | from blip.models import blip
from clip_interrogator import clip_interrogator
from pathlib import Path
from sentence_transformers import SentenceTransformer, models
import importlib
import inspect
import numpy as np
import numpy as np # linear algebra
import open_clip
import os
import os
import pandas as pd
... | code |
128016055/cell_20 | [
"text_plain_output_1.png"
] | from pathlib import Path
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
import os
import sys
from PIL import Image
from pathlib import Path
import matplotlib.pyplot as plt
from transformers import AutoProcessor, BlipForConditionalGeneration
import numpy as np
i... | code |
128016055/cell_29 | [
"text_html_output_1.png"
] | from blip.models import blip
from clip_interrogator import clip_interrogator
from pathlib import Path
from sentence_transformers import SentenceTransformer, models
import importlib
import inspect
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as p... | code |
128016055/cell_14 | [
"text_plain_output_1.png"
] | from blip.models import blip
from clip_interrogator import clip_interrogator
import importlib
import inspect
blip_path = inspect.getfile(blip)
fin = open(blip_path, 'rt')
data = fin.read()
data = data.replace("BertTokenizer.from_pretrained('bert-base-uncased')", "BertTokenizer.from_pretrained('/kaggle/input/clip-in... | code |
128016055/cell_10 | [
"text_plain_output_1.png"
] | !pip install --no-index --no-deps /kaggle/input/lavis-pretrained/salesforce-lavis/transformers* -q | code |
88102865/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
df_eval | code |
88102865/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
def create_labels(sent... | code |
88102865/cell_20 | [
"text_html_output_1.png"
] | from simpletransformers.classification import ClassificationModel, ClassificationArgs
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2... | code |
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