path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
121152202/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import tensorflow as tf
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df =... | code |
121152202/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df = pd.concat([df, df_add])
... | code |
121152202/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
print(f'the competition dataset shape is {df.shape}') | code |
121152202/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'Ceme... | code |
121152202/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split, KFold
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import lightgbm as lgbm
import tensorflow as tf
import os
f... | code |
121152202/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df = pd.concat([df, df_add])
... | code |
121152202/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df = pd.concat([df, df_add])
... | code |
121152202/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
print(f'the addition dataset ... | code |
121152202/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'C... | code |
121152202/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import tensorflow as tf
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df =... | code |
121152202/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'C... | code |
121152202/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df = pd.concat([df, df_add])
... | code |
128033768/cell_13 | [
"text_html_output_1.png"
] | from keras.layers import LSTM, Dense
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas... | code |
128033768/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x1b[31m'
GREEN ... | code |
128033768/cell_4 | [
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x1b[31m'
GREEN = '\x1b[32m'
def info(self, message: str):
pa... | code |
128033768/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x1b[31m'
GREEN = '\x1b[32m'
def info(self, message: str):
pa... | code |
128033768/cell_11 | [
"text_html_output_1.png"
] | from keras.layers import LSTM, Dense
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x... | code |
128033768/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.arima_model imp... | code |
128033768/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x1b[31m'
GREEN = '\x1b[32m'
def info(self, message: str):
pa... | code |
128033768/cell_8 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x1b[31m'
GREEN = '\x1b[32m'
def info(self, message: str):
pa... | code |
128033768/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x1b[31m'
GREEN = '\x1b[32m'
def info(self, message: str):
pa... | code |
128033768/cell_12 | [
"text_html_output_1.png"
] | from keras.layers import LSTM, Dense
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x... | code |
128033768/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
class Logger:
RESET = '\x1b[0m'
RED = '\x1b[31m'
GREEN = '\x1b[32m'
def info(self, message: str):
pa... | code |
72094126/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/heart-disease-uci/heart.csv')
data.info() | code |
72094126/cell_6 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
tree = RandomForestClassifier()
tree.fit(xtrain, ytrain)
ypred = tree.predict(xtest)
print('Prediction Accuracy', metrics.accuracy_score(ytest, ypred)) | code |
72094126/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 |
72094126/cell_7 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/heart-disease-uci/heart.csv')
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
tree = RandomForestCl... | code |
72094126/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/heart-disease-uci/heart.csv')
data.head() | code |
122263483/cell_6 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"text_plain_output_445.png",
"text_plain_output_201.png",
"text_plain_output_586.png",
"text_plain_output_261.png",
"text_plain_output_565.png",
"text_plain_output_522.png",
"text_plain_output_84.png",
"text_plain_output_521.png",
"text_p... | from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflo... | code |
122263483/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
import tensorflow as tf
import keras_tuner as kt
import os
for dirname, _, fil... | code |
122263483/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_parquet('/kaggle/input/sampled-datasets-v2/NF-UNSW-NB15-V2.parquet')
columns_to_remove = ['L4_SR... | code |
33111267/cell_21 | [
"text_plain_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.manifold import TSNE
from unidecode import unidecode
import bs4
import matplotlib.pyplot as plt
import nltk
import pandas as pd
import pandas as pd
import seaborn as... | code |
33111267/cell_13 | [
"text_plain_output_1.png"
] | from nltk import word_tokenize
from unidecode import unidecode
import bs4
import nltk
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import string
import unicodedata
pd.options.display.max_colwidth = 255
df = pd.read_csv('../input/airline-sentiment/Tweets.csv')
import sea... | code |
33111267/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
pd.options.display.max_colwidth = 255
df = pd.read_csv('../input/airline-sentiment/Tweets.csv')
df['airline_sentiment'].value_counts() | code |
33111267/cell_25 | [
"text_plain_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from sklearn.mixture import GaussianMixture
from sklearn.neighbors import NearestNeighbors
from unidecode import... | code |
33111267/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
pd.options.display.max_colwidth = 255
df = pd.read_csv('../input/airline-sentiment/Tweets.csv')
df.head(2) | code |
33111267/cell_23 | [
"text_plain_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from sklearn.neighbors import NearestNeighbors
from unidecode import unidecode
import bs4
import matplotlib.pyp... | code |
33111267/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.manifold import TSNE
from unidecode import unidecode
import bs4
import matplotlib.pyplot as plt
import nltk
import pandas as pd
import pandas as pd
import seaborn as... | code |
33111267/cell_6 | [
"text_plain_output_1.png"
] | import tensorflow_hub as hub
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
print('module %s loaded' % module_url)
def embed(input):
return model(input) | code |
33111267/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from sklearn.mixture import GaussianMixture
from sklearn.neighbors import NearestNeighbors
from unidecode import... | code |
33111267/cell_26 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
pd.options.display.max_colwidth = 255
df = pd.read_csv('../input/airline-sentiment/Tweets.csv')
comments = df['text']
import seaborn as sns
df_neg = df.query("airline_sentiment == 'negative'").head(1000).co... | code |
33111267/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
pd.options.display.max_colwidth = 255
df = pd.read_csv('../input/airline-sentiment/Tweets.csv')
print('Head: ', df.columns)
print('\nShape: ', df.shape)
print('\nDescrição:')
print(df.describe()) | code |
33111267/cell_18 | [
"text_plain_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.manifold import TSNE
from unidecode import unidecode
import bs4
import nltk
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
impo... | code |
33111267/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from sklearn.mixture import GaussianMixture
from sklearn.neighbors import NearestNeighbors
from unidecode import... | code |
33111267/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
pd.options.display.max_colwidth = 255
df = pd.read_csv('../input/airline-sentiment/Tweets.csv')
import seaborn as sns
sns.countplot(x='airline_sentiment', data=df) | code |
33111267/cell_16 | [
"text_plain_output_1.png"
] | from nltk import word_tokenize
from sklearn.manifold import TSNE
from unidecode import unidecode
import bs4
import nltk
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import string
import tensorflow_hub as hub
import unicodedata
pd.options.display.max_colwidth = 255
df =... | code |
33111267/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from sklearn.mixture import GaussianMixture
from sklearn.neighbors import NearestNeighbors
from unidecode import... | code |
33111267/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from unidecode import unidecode
import bs4
import nltk
import pandas as pd
import pandas as pd
import seaborn... | code |
33111267/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
pd.options.display.max_colwidth = 255
df = pd.read_csv('../input/airline-sentiment/Tweets.csv')
import seaborn as sns
df_neg = df.query("airline_sentiment == 'negative'").head(1000).copy()
df_neu = df.query("airline_sentiment == '... | code |
33111267/cell_27 | [
"image_output_1.png"
] | from nltk import word_tokenize
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from sklearn.mixture import GaussianMixture
from sklearn.neighbors import NearestNeighbors
from unidecode import... | code |
33111267/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
pd.options.display.max_colwidth = 255
df = pd.read_csv('../input/airline-sentiment/Tweets.csv')
comments = df['text']
comments.head(30) | code |
129001574/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.spatial.distance import squareform
from scipy.cluster.hierarchy import dendrogram, linkage
imp... | code |
129001574/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
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 warnings
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.spatial.distance import squareform
fr... | code |
129001574/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.spatial.distance import squareform
from scipy.cluster.hierarchy import dendrogram, linkage
imp... | code |
129001574/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 |
129001574/cell_7 | [
"text_plain_output_1.png"
] | pip install spektral | code |
129001574/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from spektral.layers import GCNConv
from spektral.layers import GCNConv
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Input, Dropout, Dense,Reshape,GlobalMaxPool1D,MaxPool1D
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
import num... | code |
129001574/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)
import warnings
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.spatial.distance import squareform
from scipy.cluster.hierarchy import dendrogram, linkage
imp... | code |
73079164/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 |
73079164/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import optuna
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
data_submission = pd.read_... | code |
17115291/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].... | code |
17115291/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 seaborn as sns
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].... | code |
17115291/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].... | code |
17115291/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
17115291/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
data | code |
17115291/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].... | code |
17115291/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].replace('.', '') | code |
17115291/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].... | code |
17115291/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].... | code |
17115291/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].replace('.', '')
data.Attendance = pd.to_numeric(data.A... | code |
17115291/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/WorldCups.csv')
data.rename(columns={'Runners-Up': 'RunnersUp'}, inplace=True)
for i in range(len(data.Attendance)):
data.Attendance[i] = data.Attendance[i].replace('.', '')
data.Attendance = pd.to_numeric(data.A... | code |
17115291/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/WorldCups.csv')
data.info() | code |
72112989/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
y = iris.target
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model.fit(x_train... | code |
72112989/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
import pandas as pd
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
y = iris.target
x.isnull().sum()
x.describe() | code |
72112989/cell_25 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix,classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
y_train_pred = model.predict(x... | code |
72112989/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix,classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
print(accuracy_score(y_test, y... | code |
72112989/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
import pandas as pd
import seaborn as sns
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
y = iris.target
x.isnull().sum()
sns.relplot(x='petal length (cm)', y='petal width (cm)', data=x) | code |
72112989/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
import pandas as pd
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
y = iris.target
x.info() | code |
72112989/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model.fit(x_train, y_train) | code |
72112989/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import pandas as pd
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
y = iris.target
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0... | code |
72112989/cell_28 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix,classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
print(classification_report(y_... | code |
72112989/cell_8 | [
"image_output_1.png"
] | from sklearn.datasets import load_iris
import pandas as pd
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
y = iris.target
x.isnull().sum() | code |
72112989/cell_15 | [
"text_html_output_1.png"
] | print(x_train.shape)
print(x_test.shape) | code |
72112989/cell_35 | [
"text_plain_output_1.png"
] | from IPython.display import Image
from six import StringIO
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import numpy as np
import pandas as pd
import pydotplus
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feat... | code |
72112989/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
data = []
print('enter specifications: ')
for i in range(4):
if i == 0:
print('SepalLengthCm:')
elif i == 1:
print('SepalWidthCm:')
elif i == 2:
print('PetalLengthCm:')
elif i == 3:
print('PetalWidthCm:')
n = float(input())
data.append(n)
data ... | code |
72112989/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
import pandas as pd
import seaborn as sns
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
y = iris.target
x.isnull().sum()
sns.relplot(x='sepal length (cm)', y='sepal width (cm)', data=x) | code |
72112989/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix,classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
print(confusion_matrix(y_test,... | code |
72112989/cell_37 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.savefig('img.png') | code |
72112989/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
import pandas as pd
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
y = iris.target
x.isnull().sum()
x.corr() | code |
72112989/cell_5 | [
"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 sklearn.datasets import load_iris
import pandas as pd
iris = load_iris()
x = pd.DataFrame(data=iris.data, columns=iris.feature_names)
print(x.head())
y = iris.target
print(y) | code |
1005915/cell_4 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
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 plotly.graph_objs as go
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
imp... | code |
1005915/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1005915/cell_3 | [
"text_html_output_2.png"
] | from plotly.offline import iplot, init_notebook_mode
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plot... | code |
74056627/cell_21 | [
"text_plain_output_1.png"
] | df[0] = df[0].apply(str) | code |
74056627/cell_9 | [
"text_plain_output_1.png"
] | code | |
74056627/cell_23 | [
"text_plain_output_1.png"
] | df = pd.DataFrame({'a': np.random.rand(10000), 'b': np.random.rand(10000)}) | code |
74056627/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
s = pd.Series(range(10000))
# Memory saving function credit to https://www.kaggle.com/gemartin/load-data-reduce-memory-usage
def reduce_mem_usage(df):
""" iterate through all the columns of a dataframe and modify the data type
to reduce memory usage.
"""
sta... | code |
74056627/cell_20 | [
"text_plain_output_1.png"
] | df = pd.DataFrame(pd.date_range(start='1/1/2000', end='1/08/2018')) | code |
74056627/cell_6 | [
"text_plain_output_1.png"
] | code | |
74056627/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
def reduce_mem_usage(df):
""" iterate through all the columns of a dataframe and modify the data type
to reduce memory usage.
"""
start_mem = df.memory_usage().sum() / 1024 ** 2
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
for col in df.columns:
... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.