path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89143018/cell_9 | [
"image_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import numpy as np
from pyspark import SparkContext, SparkFiles
from pyspark.sql import SparkSession
import string
import matplotlib.pyplot as plt
from pyspark... | code |
89143018/cell_25 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
from pyspark import SparkContext, ... | code |
89143018/cell_4 | [
"text_plain_output_1.png"
] | !pip install pyspark
!pip install -U -q PyDrive
!apt install openjdk-8-jdk-headless -qq --yes
import os
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64" | code |
89143018/cell_20 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import numpy as... | code |
89143018/cell_6 | [
"image_output_1.png"
] | from pyspark.sql import SparkSession
import numpy as np
from pyspark import SparkContext, SparkFiles
from pyspark.sql import SparkSession
import string
import matplotlib.pyplot as plt
from pyspark.sql.functions import split
from pyspark.sql.functions import col
from pyspark.sql.types import IntegerType
spark = SparkSe... | code |
89143018/cell_26 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
from pyspark import SparkContext, ... | code |
89143018/cell_19 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.regression import LinearRegression
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
fr... | code |
89143018/cell_18 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.regression import LinearRegression
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
fr... | code |
89143018/cell_8 | [
"image_output_1.png"
] | from pyspark.sql import SparkSession
import numpy as np
from pyspark import SparkContext, SparkFiles
from pyspark.sql import SparkSession
import string
import matplotlib.pyplot as plt
from pyspark.sql.functions import split
from pyspark.sql.functions import col
from pyspark.sql.types import IntegerType
spark = SparkSe... | code |
89143018/cell_15 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import numpy as np
from pyspark import SparkContext, SparkF... | code |
89143018/cell_16 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import numpy as np
from pyspark import SparkContext, SparkFiles
from pyspark.sql import SparkSession
import str... | code |
89143018/cell_17 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import numpy as np
from pyspark import SparkContext, SparkFiles... | code |
89143018/cell_24 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
from pyspark import SparkContext, ... | code |
89143018/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import numpy as np
from pyspark import SparkContext, SparkF... | code |
89143018/cell_22 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.regression import LinearRegression
from pyspark.ml.regression import LinearRegression
from pyspark.sql imp... | code |
89143018/cell_10 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import split
from pyspark.sql.types import IntegerType
import numpy as np
from pyspark import SparkContext, SparkFiles
from pyspark.sql import SparkSession
import string
import matplotlib.pyplot as plt
from pyspark... | code |
89143018/cell_5 | [
"image_output_1.png"
] | from pyspark.sql import SparkSession
import numpy as np
from pyspark import SparkContext, SparkFiles
from pyspark.sql import SparkSession
import string
import matplotlib.pyplot as plt
from pyspark.sql.functions import split
from pyspark.sql.functions import col
from pyspark.sql.types import IntegerType
spark = SparkSe... | code |
128047653/cell_4 | [
"text_plain_output_1.png"
] | import os
import shutil
import numpy as np
import pandas as pd
import os
basedir = '/kaggle/input/5-flower-types-classification-dataset/flower_images'
source_path_orchid = os.path.join(basedir, 'Orchid')
source_path_sunflower = os.path.join(basedir, 'Sunflower')
source_path_tulip = os.path.join(basedir, 'Tulip')
so... | code |
128047653/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import os
import random
import shutil
import numpy as np
import pandas as pd
import os
basedir = '/kaggle/input/5-flower-types-classification-dataset/flower_images'
source_path_orchid = os.path.join(basedir, 'Orchid')
source_path_sunflower = os.path.join(basedir, 'Sunflower')
source_path_tulip = os.path.join(based... | code |
128047653/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
basedir = '/kaggle/input/5-flower-types-classification-dataset/flower_images'
print('contents of base directory:')
print(os.listdir(basedir)) | code |
128047653/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 |
128047653/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
basedir = '/kaggle/input/5-flower-types-classification-dataset/flower_images'
print(os.listdir(basedir))
source_path_orchid = os.path.join(basedir, 'Orchid')
source_path_sunflower = os.path.join(basedir, 'Sunflower')
source_path_tulip = os.path.join(basedir,... | code |
130008924/cell_21 | [
"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)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.cs... | code |
130008924/cell_13 | [
"text_html_output_1.png"
] | X_test.shape | code |
130008924/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.drop(['Alley'], axis=1)
... | code |
130008924/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)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.head() | code |
130008924/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)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.info() | code |
130008924/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.drop(['Alley'], axis=1)
... | code |
130008924/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 |
130008924/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)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns | code |
130008924/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
test = test.fillna(0)
numerical_test_cols = te... | code |
130008924/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.drop(['Alley'], axis=1) | code |
130008924/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
X_test.shape
lr = LinearRegression()
lr.fit(X_train, Y_train)
lr.score(X_test, Y_test) | code |
130008924/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
test.info() | code |
130008924/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
test = test.fillna(0)
numerical_test_cols = te... | code |
130008924/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, Y_train) | code |
130008924/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)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
test.head() | code |
105182734/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import gc
import warnings
warnings.filterwarnings('ignore')
import scipy as sp
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from tqdm.auto import tqdm... | code |
105182734/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import gc
import warnings
warnings.filterwarnings('ignore')
import scipy as sp
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from tqdm.auto import tqdm... | code |
105182734/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import gc
import warnings
warnings.filterwarnings('ignore')
import scipy as sp
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from tqdm.auto import tqdm
import itertools
fr... | code |
105182734/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import gc
import warnings
warnings.filterwarnings('ignore')
import scipy as sp
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from tqdm.auto import tqdm
import itertools
fr... | code |
16112556/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
print('MAE for train', mean_absolu... | code |
16112556/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_ | code |
16112556/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike_df = pd.read_csv('../input/bike_share.csv')
bike_df.shape | code |
16112556/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_
train_predict = model.predict(train_X)
print('MAE for train', mean_absolute_error(train_Y, train_predict)) | code |
16112556/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike_df = pd.read_csv('../input/bike_share.csv')
bike_df.shape
bike_df.isna().sum() | code |
16112556/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16112556/cell_19 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
print('MSE for test', mean_squared... | code |
16112556/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16112556/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike_df = pd.read_csv('../input/bike_share.csv')
bike_df.shape
bike_df.isna().sum()
bike_df.corr() | code |
16112556/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_
train_predict = model.predict(train_X)
print('MSE', mean_squared_error(train_Y, train_predict)) | code |
16112556/cell_14 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_X, train_Y)
model.intercept_
model.coef_ | code |
16112556/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_X, train_Y) | code |
16112556/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike_df = pd.read_csv('../input/bike_share.csv')
bike_df.shape
bike_df.head() | code |
50212949/cell_13 | [
"text_plain_output_1.png"
] | from collections import defaultdict
from nltk.corpus import stopwords
from nltk.corpus import stopwords
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
import seaborn as sns
import seaborn as sns
import warnings
im... | code |
50212949/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.corpus import stopwords
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import pandas as pd
import nltk
from nltk.stem import PorterStemmer
from nltk.t... | code |
50212949/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
tweet = pd.read_csv('../input/analytics-vidhya-identify-the-sentiments/train.csv')
tweet.head(5) | code |
50212949/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
tweet = pd.read_csv('../input/analytics-vidhya-identify-the-sentiments/train.csv')
test = pd.read_csv('../input/analytics-vidhya-identify-the-sentiments/test.csv')
print('There are {} rows and {} columns in train'.format(tweet.shape[0], tweet.shape[1]))
print('There are {} ro... | code |
50212949/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.corpus import stopwords
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import pandas as pd
import nltk
from nltk.stem import PorterStemmer
from nltk.t... | code |
50212949/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.corpus import stopwords
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import warnings
import pandas as pd
import nltk
from nltk.stem import PorterStemmer
from nltk.t... | code |
50212949/cell_10 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.corpus import stopwords
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
import seaborn as sns
import seaborn as sns
import warnings
import pandas as pd
import nltk
from nl... | code |
50212949/cell_12 | [
"text_html_output_1.png"
] | from collections import defaultdict
from nltk.corpus import stopwords
from nltk.corpus import stopwords
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
import seaborn as sns
import seaborn as sns
import warnings
im... | code |
50212949/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
tweet = pd.read_csv('../input/analytics-vidhya-identify-the-sentiments/train.csv')
test = pd.read_csv('../input/analytics-vidhya-identify-the-sentiments/test.csv')
test.head(5) | code |
104123576/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = sklearn.datasets.load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['price'] = data.target
df.info() | code |
104123576/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
data = sklearn.datasets.load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['price'] = data.target
df.head() | code |
104123576/cell_11 | [
"text_html_output_1.png"
] | from sklearn import metrics
from xgboost import XGBRegressor
model = XGBRegressor(objective='reg:squarederror')
model.fit(x_train, y_train)
pred = model.predict(x_test)
metrics.r2_score(y_test, pred)
metrics.mean_absolute_error(y_test, pred) | code |
104123576/cell_8 | [
"text_plain_output_1.png"
] | from xgboost import XGBRegressor
model = XGBRegressor(objective='reg:squarederror')
model.fit(x_train, y_train) | code |
104123576/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = sklearn.datasets.load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['price'] = data.target
df.describe() | code |
104123576/cell_10 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import metrics
from xgboost import XGBRegressor
model = XGBRegressor(objective='reg:squarederror')
model.fit(x_train, y_train)
pred = model.predict(x_test)
metrics.r2_score(y_test, pred) | code |
104123576/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = sklearn.datasets.load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['price'] = data.target
df['price'].value_counts | code |
90124333/cell_3 | [
"text_plain_output_1.png"
] | import tensorflow as tf
def auto_select_accelerator():
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except Value... | code |
90124333/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow import keras
import tensorflow as tf
def auto_select_accelerator():
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPU... | code |
2005556/cell_4 | [
"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)
ufo = pd.read_csv('../input/scrubbed.csv')
countryFreq = ufo['country'].value_counts()
labels = list(countryFreq.index)
positionsForBars = list(range(len(labels)))
plt.xticks(positionsForBars, labels)
stateFreq = ... | code |
2005556/cell_6 | [
"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
ufo = pd.read_csv('../input/scrubbed.csv')
countryFreq = ufo['country'].value_counts()
labels = list(countryFreq.index)
positionsForBars = list(range(len(labels)))
plt.xticks(positionsForBars... | code |
2005556/cell_2 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ufo = pd.read_csv('../input/scrubbed.csv')
ufo.head() | code |
2005556/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2005556/cell_3 | [
"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)
ufo = pd.read_csv('../input/scrubbed.csv')
countryFreq = ufo['country'].value_counts()
labels = list(countryFreq.index)
positionsForBars = list(range(len(labels)))
plt.bar(positionsForBars, countryFreq.values)
plt.... | code |
2005556/cell_5 | [
"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
ufo = pd.read_csv('../input/scrubbed.csv')
countryFreq = ufo['country'].value_counts()
labels = list(countryFreq.index)
positionsForBars = list(range(len(labels)))
plt.xticks(positionsForBars... | code |
1007671/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_region.unique():
for seed in range(2, 17):
res... | code |
1007671/cell_19 | [
"text_plain_output_1.png"
] | from itertools import combinations
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_re... | code |
1007671/cell_7 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_region.unique():
for seed in range... | code |
1007671/cell_15 | [
"image_output_1.png"
] | from itertools import combinations
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_re... | code |
1007671/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
df.head() | code |
1007671/cell_12 | [
"text_html_output_1.png"
] | from itertools import combinations
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_region.unique():
f... | code |
18142557/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
path = '../input/data.csv'
df = pd.read_csv(path)
df.columns
df = df.drop(['Unnamed: 0', 'ID', 'Position', 'Name', 'Photo', 'Nationality', 'Potential', 'Flag', 'Club', 'Club Logo', 'Value', 'Wage', 'Special', 'Preferred Foot', 'International Reputation', 'Weak Foot', 'Skill Moves', 'Work Rate', '... | code |
18142557/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/data.csv'
df = pd.read_csv(path)
df.head()
df.columns | code |
18142557/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
path = '../input/data.csv'
df = pd.read_csv(path)
df.columns
df = df.drop(['Unnamed: 0', 'ID', 'Position', 'Name', 'Photo', 'Nationality', 'Potential', 'Flag', 'Club', 'Club Logo', 'Value', 'Wage', 'Special', 'Preferred Foot', 'International Reputation', 'Weak Foot', 'Skill Mo... | code |
18142557/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/data.csv'
df = pd.read_csv(path)
df.columns
df = df.drop(['Unnamed: 0', 'ID', 'Position', 'Name', 'Photo', 'Nationality', 'Potential', 'Flag', 'Club', 'Club Logo', 'Value', 'Wage', 'Special', 'Preferred Foot', 'International Reputation', 'Weak Foot', 'Skill Moves', 'Work Rate', '... | code |
18142557/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import linear_model
import os
print(os.listdir('../input')) | code |
18142557/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/data.csv'
df = pd.read_csv(path)
df.columns
df = df.drop(['Unnamed: 0', 'ID', 'Position', 'Name', 'Photo', 'Nationality', 'Potential', 'Flag', 'Club', 'Club Logo', 'Value', 'Wage', 'Special', 'Preferred Foot', 'International... | code |
18142557/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/data.csv'
df = pd.read_csv(path)
df.columns
df = df.drop(['Unnamed: 0', 'ID', 'Position', 'Name', 'Photo', 'Nationality', 'Potential', 'Flag', 'Club', 'Club Logo', 'Value', 'Wage', 'Special', 'Preferred Foot', 'International Reputation', 'Weak Foot', 'Skill Moves', 'Work Rate', '... | code |
18142557/cell_18 | [
"text_plain_output_1.png"
] | train.describe() | code |
18142557/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/data.csv'
df = pd.read_csv(path)
df.columns
df = df.drop(['Unnamed: 0', 'ID', 'Position', 'Name', 'Photo', 'Nationality', 'Potential', 'Flag', 'Club', 'Club Logo', 'Value', 'Wage', 'Special', 'Preferred Foot', 'International Reputation', 'Weak Foot', 'Skill Moves', 'Work Rate', '... | code |
18142557/cell_15 | [
"text_html_output_1.png"
] | train.head() | code |
18142557/cell_16 | [
"text_plain_output_1.png"
] | validate.head() | code |
18142557/cell_17 | [
"text_plain_output_1.png"
] | test.head() | code |
18142557/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/data.csv'
df = pd.read_csv(path)
df.columns
df = df.drop(['Unnamed: 0', 'ID', 'Position', 'Name', 'Photo', 'Nationality', 'Potential', 'Flag', 'Club', 'Club Logo', 'Value', 'Wage', 'Special', 'Preferred Foot', 'International Reputation', 'Weak Foot', 'Skill Moves', 'Work Rate', '... | code |
18142557/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/data.csv'
df = pd.read_csv(path)
df.columns
df = df.drop(['Unnamed: 0', 'ID', 'Position', 'Name', 'Photo', 'Nationality', 'Potential', 'Flag', 'Club', 'Club Logo', 'Value', 'Wage', 'Special', 'Preferred Foot', 'International... | code |
34120249/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_cour... | code |
34120249/cell_25 | [
"image_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_... | code |
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