path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104124784/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis=1)
df.describe() | code |
104124784/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.head() | code |
104124784/cell_34 | [
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis... | code |
104124784/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis=1)
df.isnull().sum().sort_values(ascending=False) * 100 / df.sh... | code |
104124784/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df.head() | code |
104124784/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
print('train', train.shape)
print('test', test.shape) | code |
104124784/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis=1)
df.isnull().sum().sort_values(ascending=False) * 100 / df.sh... | code |
104124784/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis=1)
df.head() | code |
104124784/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set()
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selec... | code |
104124784/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis... | code |
104124784/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis=1)
df.info() | code |
104124784/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis=1)
df.isnull().sum().sort_values(ascending=False) * 100 / df.sh... | code |
104124784/cell_3 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.head() | code |
104124784/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis... | code |
34144131/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from multiprocessing import cpu_count
from multiprocessing.dummy import Pool
import cv2
import gluoncv as gcv
import gluoncv as gcv
import gluoncv as gcv
import json
import mxnet as mx
import mxnet as mx
import mxnet as mx
import numpy as np
import os
import os
import pandas as pd
import pandas as pd
imp... | code |
73070153/cell_21 | [
"text_plain_output_1.png"
] | from gensim.corpora import Dictionary
from gensim.models import TfidfModel, LsiModel, Word2Vec
from nltk import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
import re
stop_words = stopwords.words('english')
def clean_text(txt: str):
"""Clean and lower case text."""
txt = re.sub('[^A-Za-z0... | code |
73070153/cell_44 | [
"text_plain_output_1.png"
] | w2v_model = Word2Vec(vector_size=dim_w2v, alpha=alpha, min_alpha=alpha_min, window=wnd, min_count=mincount, sample=sample, sg=sg, negative=ngt, workers=threads)
word_freq = {dct[k]: v for k, v in dct.cfs.items()}
w2v_model.build_vocab_from_freq(word_freq)
num_samples = dct.num_docs
w2v_model.train(corpus_w2v, total_exa... | code |
73070153/cell_39 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | lsi_model = LsiModel(corpus=tfidf_model[corpus], id2word=dct, num_topics=dim_lsi) | code |
73070153/cell_26 | [
"text_plain_output_1.png"
] | word_freq = {dct[k]: v for k, v in dct.cfs.items()}
w2v_model.build_vocab_from_freq(word_freq)
num_samples = dct.num_docs
w2v_model.train(tokenized_data, total_examples=num_samples, epochs=epochs) | code |
73070153/cell_19 | [
"text_plain_output_1.png"
] | w2v_model = Word2Vec(sentences=tokenized_data, vector_size=dim_w2v, alpha=alpha, min_alpha=alpha_min, window=wnd, min_count=mincount, sample=sample, sg=sg, negative=ngt, epochs=epochs, workers=threads) | code |
73070153/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import nltk
nltk.download('stopwords')
nltk.download('punkt') | code |
73070153/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | dim_lsi = 200
lsi_model = LsiModel(corpus=tfidf_matrix, id2word=dct, num_topics=dim_lsi) | code |
16129109/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
import os
from PIL import Image
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from keras.models import Sequential, Model
from keras.layers import Dense, Conv1D, MaxPooling1D, Fl... | code |
16129109/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
import numpy as np
import os
import pandas as pd
def get_pixel_data(filepath):
"""
Get the pixel data from an image as a pandas DataFrame.
"""
image = Image.open(filepath)
pixel_data = np.array(image.getdata())
pixel_data = pixel_data.mean(axis=1)
pixel_data = pixel... | code |
16129109/cell_15 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten
from keras.models import Sequential, Model
from keras.utils import to_categorical
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import numpy as np
import os
import pandas... | code |
16129109/cell_17 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten
from keras.models import Sequential, Model
from keras.utils import to_categorical
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import numpy as np
import os
import pandas... | code |
16129109/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from sklearn.linear_model import LogisticRegression
import numpy as np
import os
import pandas as pd
import random
def get_pixel_data(filepath):
"""
Get the pixel data from an image as a pandas DataFrame.
"""
image = Image.open(filepath)
pixel_data = np.array(image.getdat... | code |
16129109/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import numpy as np
import os
import pandas as pd
import random
def get_pixel_data(filepath):
"""
Get the pixel data from an image as a pandas DataFrame.
"""
image = Image.... | code |
128017800/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
popularityByGenr... | code |
128017800/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
popularityByGenr... | code |
128017800/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
popularityByGenre = track_csv.groupby([... | code |
128017800/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
popularityByGenr... | code |
128017800/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128017800/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
popularityByGenre = track_csv.groupby([... | code |
128017800/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
popularityByGenre = track_csv.groupby([... | code |
128017800/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
popularityByGenr... | code |
128017800/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
track_csv.head() | code |
128017800/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
popularityByGenr... | code |
128017800/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
track_csv = pd.read_table('/kaggle/input/-spotify-tracks-dataset/dataset.csv', sep=',')
track_csv = track_csv.rename(columns={'duration_ms': 'duration'})
track_csv['duration'] = track_csv['duration'] / 60000
track_csv['time_signature'].value_count... | code |
73096770/cell_9 | [
"image_output_1.png"
] | import os
import pandas as pd
product_name_dictionary = {}
product_name_dictionary2 = {}
for dirname, _, filenames in os.walk('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/engagement_data/'):
for filename in filenames:
engagement_data_path = os.path.join(dirname, filename)
df_tem... | code |
73096770/cell_11 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
product_name_dictionary = {}
product_name_dictionary2 = {}
for dirname, _, filenames in os.walk('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/engagement_data/'):
for filename in filenames:
engagement_data_path = os.path.join(... | code |
73096770/cell_16 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
product_name_dictionary = {}
product_name_dictionary2 = {}
for dirname, _, filenames in os.walk('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/engagement_data/'):
for filename in filenames:
engagement_data_path = os.path.join(... | code |
73096770/cell_14 | [
"image_output_1.png"
] | import os
import pandas as pd
product_name_dictionary = {}
product_name_dictionary2 = {}
for dirname, _, filenames in os.walk('/kaggle/input/learnplatform-covid19-impact-on-digital-learning/engagement_data/'):
for filename in filenames:
engagement_data_path = os.path.join(dirname, filename)
df_tem... | code |
105197461/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular... | code |
105197461/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/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 |
105197461/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105197461/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2022/test.csv')
sample_submission_df = pd.read_csv('/kaggle/input/tabular-playground-series-aug-... | code |
105204825/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_comp = pd.read_csv('../input/big-data-derby-2022/nyra_2019_complete.csv')
df_race = pd.read_csv('../input/big-data-derby-2022/nyra_race_table.csv')
df_stsrt = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
df_track = pd.read_cs... | code |
105204825/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | df_tracj.head(2) | code |
105204825/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_comp = pd.read_csv('../input/big-data-derby-2022/nyra_2019_complete.csv')
df_race = pd.read_csv('../input/big-data-derby-2022/nyra_race_table.csv')
df_stsrt = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
df_track = pd.read_cs... | code |
105204825/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 |
105204825/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_comp = pd.read_csv('../input/big-data-derby-2022/nyra_2019_complete.csv')
df_race = pd.read_csv('../input/big-data-derby-2022/nyra_race_table.csv')
df_stsrt = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
df_track = pd.read_cs... | code |
105204825/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_comp = pd.read_csv('../input/big-data-derby-2022/nyra_2019_complete.csv')
df_race = pd.read_csv('../input/big-data-derby-2022/nyra_race_table.csv')
df_stsrt = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
df_track = pd.read_cs... | code |
1005892/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df.info() | code |
1005892/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import SGDClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'S... | code |
1005892/cell_20 | [
"text_html_output_1.png"
] | from sklearn.linear_model import SGDClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'S... | code |
1005892/cell_2 | [
"application_vnd.jupyter.stderr_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 |
1005892/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import SGDClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'S... | code |
1005892/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'Sex', 'SibSp', 'Cabin', 'Embarked'])
train_y = tr... | code |
1005892/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import SGDClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'S... | code |
1005892/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'Sex', 'SibSp', 'Cabin', 'Embarked'])
train_y = tr... | code |
1005892/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'Sex', 'SibSp', 'Cabin', 'Embarked'])
train_y = tr... | code |
1005892/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'Sex', 'SibSp', 'Cabin', 'Embarked'])
train_y = tr... | code |
1005892/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df[['Sex', 'Survived']].groupby(['Sex', 'Survived']).size() | code |
1005892/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import SGDClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
model_cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
train_x = pd.get_dummies(train_df[model_cols], columns=['Pclass', 'S... | code |
106202249/cell_1 | [
"text_plain_output_1.png"
] | !pip install -q timm
!pip install -q einops | code |
129021628/cell_21 | [
"image_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
import warnings
import warnings
warnings.simplefilter('ignore')
plt.style.use('seaborn')
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.... | code |
129021628/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.DataFrame(Data)
df
df.describe().T
is_nan = df.isna().sum().to_frame(name='Count of nan')
is_nan | code |
129021628/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.DataFrame(Data)
df | code |
129021628/cell_25 | [
"text_html_output_10.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_9.png",
"text_html_output_8.png",
"text_html_output_3.png",
"text_html_output_7.png"
] | pip install kneed | code |
129021628/cell_20 | [
"image_output_1.png"
] | from termcolor import colored
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 plotly.express as px
import seaborn as sns
import warnings
import warnings
warnings.simplefilter('ignore')
plt.style.use('seaborn')
Data = pd.read_csv('... | code |
129021628/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from termcolor import colored
import plotly.express as px
import kaleido
from sklearn.preprocessing import StandardScaler
import matplotlib.image as mpimg
from sklearn.cluster import KMeans
from sklearn.cluster import DBSCAN
fr... | code |
129021628/cell_26 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_9.png",
"text_html_output_1.png",
"text_html_output_8.png",
"text_html_output_3.png",
"text_html_output_7.png"
] | from kneed import KneeLocator
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.re... | code |
129021628/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.DataFrame(Data)
df
df.describe().T
df.info() | code |
129021628/cell_19 | [
"image_output_1.png"
] | from termcolor import colored
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 plotly.express as px
import seaborn as sns
import warnings
import warnings
warnings.simplefilter('ignore')
plt.style.use('seaborn')
Data = pd.read_csv('... | code |
129021628/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 |
129021628/cell_18 | [
"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
import warnings
import warnings
warnings.simplefilter('ignore')
plt.style.use('seaborn')
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.... | code |
129021628/cell_15 | [
"text_html_output_1.png"
] | from termcolor import colored
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.DataFrame(Data)
df
df.describe().T
is_nan = df.isna().sum().to_frame(name='Count of nan')
is_nan
print(colored(f'... | code |
129021628/cell_16 | [
"text_html_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
import warnings
import warnings
warnings.simplefilter('ignore')
plt.style.use('seaborn')
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.... | code |
129021628/cell_17 | [
"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
import warnings
import warnings
warnings.simplefilter('ignore')
plt.style.use('seaborn')
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.... | code |
129021628/cell_14 | [
"text_plain_output_1.png"
] | from termcolor import colored
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.DataFrame(Data)
df
df.describe().T
is_nan = df.isna().sum().to_frame(name='Count of nan')
is_nan
print(colored(f'... | code |
129021628/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.DataFrame(Data)
df
df.describe().T | code |
129021628/cell_27 | [
"text_html_output_1.png"
] | from kneed import KneeLocator
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot ... | code |
129021628/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Data = pd.read_csv('/kaggle/input/country-dataset/Country_Dataset.csv')
df = pd.DataFrame(Data)
df
df.describe().T
df.describe(include=['object']) | code |
129021628/cell_5 | [
"image_output_1.png"
] | pip install kaleido | code |
16111583/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import unittest
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
import pandas as pd
from collections import defaultdict
import time
import unittest
t = unittest.TestCase()
SPACE_DIMENSIONS = 2
class Points(np.nd... | code |
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