path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50218411/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
df.head() | code |
50218411/cell_23 | [
"image_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
df = df.drop('enrollee_id', axis=1)
fig= plt.figure(figsize=(25, 16))
i = 1
for val ... | code |
50218411/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
df = df.drop('enrollee_id', axis=1)
df.describe() | code |
50218411/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
df = df.drop('enrollee_id', axis=1)
fig= plt.figure(figsize=(25, 16))
i = 1
for val in df.columns:
if val not in ["city", "city_development_index", "t... | code |
50218411/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
df = df.drop('enrollee_id', axis=1)
df.head() | code |
50218411/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
df = df.drop('enrollee_id', axis=1)
fig= plt.figure(figsize=(25, 16))
i = 1
for val ... | code |
50218411/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
df = df.drop('enrollee_id', axis=1)
for i in df.columns:
print(df[i].value_counts())
print('----------------') | code |
50218411/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
df = df.drop('enrollee_id', axis=1)
fig = plt.figure(figsize=(25, 16))
i = 1
for val in df.columns:
if val not in ['city', 'city_development_index', 't... | code |
1005676/cell_9 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df = pd.read_csv('../input/movie_met... | code |
1005676/cell_23 | [
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df ... | code |
1005676/cell_11 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df = pd.read_csv('../input/movie_met... | code |
1005676/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df ... | code |
1005676/cell_7 | [
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df = pd.read_csv('../input/movie_met... | code |
1005676/cell_15 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df ... | code |
1005676/cell_3 | [
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input'... | code |
1005676/cell_17 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df ... | code |
1005676/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df ... | code |
1005676/cell_12 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df ... | code |
1005676/cell_5 | [
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style='white')
import matplotlib.pyplot as plt
from subprocess import check_output
df = pd.read_csv('../input/movie_met... | code |
34119229/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/fifa19/data.csv')
data = pd.read_csv('/kaggle/input/fifa19/data.csv')
data.columns | code |
34119229/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/fifa19/data.csv')
df.head() | code |
34119229/cell_19 | [
"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
df = pd.read_csv('/kaggle/input/fifa19/data.csv')
data = pd.read_csv('/kaggle/input/fifa19/data.csv')
data.columns
plt.rcParams['figure.figsize'] = (25, 16)
plt.rcParams['font.family'] = 't... | code |
34119229/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
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34119229/cell_18 | [
"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)
df = pd.read_csv('/kaggle/input/fifa19/data.csv')
data = pd.read_csv('/kaggle/input/fifa19/data.csv')
data.columns
df = pd.read_csv('/kaggle/input/fifa19/data.csv')
threshold1 = sum(data.Overall) / len(data.Overa... | code |
34119229/cell_17 | [
"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)
df = pd.read_csv('/kaggle/input/fifa19/data.csv')
data = pd.read_csv('/kaggle/input/fifa19/data.csv')
data.columns
df = pd.read_csv('/kaggle/input/fifa19/data.csv')
threshold1 = sum(data.Overall) / len(data.Overa... | code |
34119229/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/fifa19/data.csv')
data = pd.read_csv('/kaggle/input/fifa19/data.csv')
data.columns
data.info() | code |
72087083/cell_13 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/source-based-news-classification/news_articles.csv')
df.isnull().sum()
df = df.dropna()
text = ' '.join(df['text_without... | code |
72087083/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/source-based-news-classification/news_articles.csv')
df.isnull().sum() | code |
72087083/cell_4 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
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 |
72087083/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/source-based-news-classification/news_articles.csv')
df.head() | code |
72087083/cell_15 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/source-based-news-classification/news_articles.csv')
df.isnull... | code |
72087083/cell_16 | [
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/source-based-news-classification/news_articles.csv')
df.isnull... | code |
72087083/cell_17 | [
"image_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/source-based-news-classification/news_articles.csv')
df.isnull... | code |
72087083/cell_14 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/source-based-news-classification/news_articles.csv')
df.isnull().sum()
df = df.dropna()
text = ' '.join(df['text_without... | code |
72087083/cell_12 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/source-based-news-classification/news_articles.csv')
df.isnull().sum()
df = df.dropna()
text = ' '.join(df['text_without... | code |
105188272/cell_13 | [
"text_plain_output_1.png"
] | from transformers import pipeline
sentiment_analyzer = pipeline('sentiment-analysis') | code |
105188272/cell_9 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, facial recognition technology is now the talk of the town. Ranging from privacy concerns to a curious boyfriend unlocking his girlfriend’s phone while she sle... | code |
105188272/cell_25 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
from heapq import nlargest
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
from transformers import pipeline
import spacy
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, ... | code |
105188272/cell_4 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6') | code |
105188272/cell_23 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
from heapq import nlargest
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
from transformers import pipeline
import spacy
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, ... | code |
105188272/cell_20 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from happytransformer import HappyTextToText
from transformers import pipeline
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, facial recognition technology is now the talk of the town. Ranging from privacy concerns to a curious boyfriend unlocking h... | code |
105188272/cell_26 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
from heapq import nlargest
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
from transformers import pipeline
import spacy
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, ... | code |
105188272/cell_2 | [
"text_plain_output_1.png"
] | !pip install happytransformer | code |
105188272/cell_7 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, facial recognition technology is now the talk of the town. Ranging from privacy concerns to a curious boyfriend unlocking his girlfriend’s phone while she sle... | code |
105188272/cell_18 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
from transformers import pipeline
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, facial recognition technology is now the talk of the town. Ranging from privacy concerns to a curious boyfriend unlocking h... | code |
105188272/cell_15 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
from transformers import pipeline
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, facial recognition technology is now the talk of the town. Ranging from privacy concerns to a curious boyfriend unlocking h... | code |
105188272/cell_24 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
from heapq import nlargest
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
from transformers import pipeline
import spacy
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, ... | code |
105188272/cell_10 | [
"text_plain_output_1.png"
] | from happytransformer import HappyTextToText
happy_tt1 = HappyTextToText('BERT', 'sshleifer/distilbart-cnn-12-6')
text = '\nIn light of recent news from Apple, facial recognition technology is now the talk of the town. Ranging from privacy concerns to a curious boyfriend unlocking his girlfriend’s phone while she sle... | code |
34146352/cell_30 | [
"text_html_output_1.png"
] | from pandasql import sqldf
import pandas as pd
import plotly.express as px
df_villagers = pd.read_csv('../input/animal-crossing/villagers.csv', encoding='utf-8')
df_villagers.drop(columns=['id', 'row_n', 'phrase', 'full_id', 'url'])
species = sqldf("SELECT species, COUNT(species) AS size FROM df_villagers GROUP BY... | code |
34146352/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
df_villagers = pd.read_csv('../input/animal-crossing/villagers.csv', encoding='utf-8')
df_items = pd.read_csv('../input/animal-crossing/items.csv', encoding='utf-8')
df_items.head()
df_items.drop(columns=['num_id', 'id', 'orderable', 'sources', 'customizable', 'recipe', 'recipe_id', 'games_id', '... | code |
34146352/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df_villagers = pd.read_csv('../input/animal-crossing/villagers.csv', encoding='utf-8')
df_villagers.head()
df_villagers.drop(columns=['id', 'row_n', 'phrase', 'full_id', 'url']) | code |
34146352/cell_29 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from pandasql import sqldf
import pandas as pd
import plotly.express as px
df_villagers = pd.read_csv('../input/animal-crossing/villagers.csv', encoding='utf-8')
df_villagers.drop(columns=['id', 'row_n', 'phrase', 'full_id', 'url'])
species = sqldf("SELECT species, COUNT(species) AS size FROM df_villagers GROUP BY... | code |
34146352/cell_8 | [
"text_html_output_1.png"
] | from pandasql import sqldf
import plotly.express as px
species = sqldf('SELECT species, COUNT(species) AS size FROM df_villagers GROUP BY species ORDER BY size DESC')
pie = px.pie(species, values='size', names='species', title='Villager Species', color_discrete_sequence=px.colors.qualitative.Dark24)
pie.show()
barh =... | code |
34146352/cell_38 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from pandasql import sqldf
import pandas as pd
import plotly.express as px
df_villagers = pd.read_csv('../input/animal-crossing/villagers.csv', encoding='utf-8')
df_villagers.drop(columns=['id', 'row_n', 'phrase', 'full_id', 'url'])
species = sqldf("SELECT species, COUNT(species) AS size FROM df_villagers GROUP BY... | code |
34146352/cell_35 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from pandasql import sqldf
import pandas as pd
import plotly.express as px
df_villagers = pd.read_csv('../input/animal-crossing/villagers.csv', encoding='utf-8')
df_villagers.drop(columns=['id', 'row_n', 'phrase', 'full_id', 'url'])
species = sqldf("SELECT species, COUNT(species) AS size FROM df_villagers GROUP BY... | code |
34146352/cell_14 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from pandasql import sqldf
import pandas as pd
import plotly.express as px
df_villagers = pd.read_csv('../input/animal-crossing/villagers.csv', encoding='utf-8')
df_villagers.drop(columns=['id', 'row_n', 'phrase', 'full_id', 'url'])
species = sqldf("SELECT species, COUNT(species) AS size FROM df_villagers GROUP BY... | code |
34146352/cell_10 | [
"text_html_output_1.png"
] | from pandasql import sqldf
import plotly.express as px
species = sqldf("SELECT species, COUNT(species) AS size FROM df_villagers GROUP BY species ORDER BY size DESC")
pie = px.pie(species, values='size', names='species', title='Villager Species', color_discrete_sequence=px.colors.qualitative.Dark24,)
pie.show()
barh... | code |
34146352/cell_37 | [
"text_html_output_1.png"
] | from pandasql import sqldf
resale = sqldf('SELECT category,sell_value, buy_value from df_items')
resale = resale.dropna()
resale['resale'] = resale['sell_value'] / resale['buy_value'] * 100
resale_categories = sqldf('SELECT category, AVG(resale) AS avg_resale from resale GROUP BY category ORDER BY avg_resale DESC')
re... | code |
34146352/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from pandasql import sqldf
import plotly.express as px
species = sqldf("SELECT species, COUNT(species) AS size FROM df_villagers GROUP BY species ORDER BY size DESC")
pie = px.pie(species, values='size', names='species', title='Villager Species', color_discrete_sequence=px.colors.qualitative.Dark24,)
pie.show()
barh... | code |
18128635/cell_4 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import pandas_profiling as pp
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_colwidth', -1)
# Função reduce memory -> https://www.kaggle.com/cttsai
def reduce_mem_us... | code |
18128635/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import pandas_profiling as pp
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_colwidth', -1)
def reduce_mem_usage(df, verbose=True):
start_mem = df.memory_usage().su... | code |
18128635/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import pandas_profiling as pp
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_colwidth', -1)
# Função reduce memory -> https://www.kaggle.com/cttsai
def reduce_mem_us... | code |
2010736/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'... | code |
2010736/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
sale_price_column = data.SalePrice
two_columns = ['Alley', 'LotShape']
two_columns_data = data[two_columns]
from sklearn.tree import Dec... | code |
2010736/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
sale_price_column = data.SalePrice... | code |
2010736/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_fi... | code |
2010736/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomF... | code |
2010736/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
print(data.describe())
sale_price_column = data.SalePrice
print(sale_price_column.head())
two_columns = ['Alley', 'LotShape']
two_columns_data = data[two_columns]
print(two_columns_data.describe()) | code |
2010736/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'... | code |
2010736/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
sale_price_column = data.SalePrice
two_columns = ['Alley', 'LotShape']
two_columns_data ... | code |
2007899/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')
var = 'GrLivArea'
plt... | code |
2007899/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')
var = 'GrLivArea'
plt = pd.concat([train['Sa... | code |
2007899/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
train['SalePrice'].describe() | code |
2007899/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')
sns.distplot(train['SalePrice']) | code |
2007899/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.head() | code |
2007899/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')
var = 'GrLivArea'
plt... | code |
2007899/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')
print('Skewness: %f' % train['SalePrice'].skew())
print('kurtosis: %f' % trai... | code |
2007899/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')
var = 'GrLivArea'
plt = pd.concat([train['Sa... | code |
2007899/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
print(train.dtypes)
train.columns | code |
2007899/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')
var = 'GrLivArea'
plt... | code |
2007899/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')
var = 'GrLivArea'
plt... | code |
2007899/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')
train.columns
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending=False)[:10], '\n') | code |
34124852/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/gun-violence-data/gun-violence-data_01-2013_03-2018.csv')
p_null = (len(df) - df.count()) * 100.0 / len(df)
p_null
train = df[['date', 's... | code |
34124852/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/gun-violence-data/gun-violence-data_01-2013_03-2018.csv')
df.head() | code |
34124852/cell_6 | [
"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)
df = pd.read_csv('/kaggle/input/gun-violence-data/gun-violence-data_01-2013_03-2018.csv')
p_null = (len(df) - df.count()) * 100.0 / len(df)
p_null
train = df[['date', 'state', 'city_or_county', 'address', 'n_killed', 'n_injur... | code |
34124852/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)
df = pd.read_csv('/kaggle/input/gun-violence-data/gun-violence-data_01-2013_03-2018.csv')
p_null = (len(df) - df.count()) * 100.0 / len(df)
p_null
train = df[['date', 'state', 'city_or_county', 'address', 'n_killed', 'n_injur... | code |
34124852/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 |
34124852/cell_7 | [
"text_plain_output_1.png",
"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
df = pd.read_csv('/kaggle/input/gun-violence-data/gun-violence-data_01-2013_03-2018.csv')
p_null = (len(df) - df.count()) * 100.0 / len(df)
p_null
train = df[['date', 's... | code |
34124852/cell_8 | [
"text_plain_output_1.png",
"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
df = pd.read_csv('/kaggle/input/gun-violence-data/gun-violence-data_01-2013_03-2018.csv')
p_null = (len(df) - df.count()) * 100.0 / len(df)
p_null
train = df[['date', 's... | code |
34124852/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/gun-violence-data/gun-violence-data_01-2013_03-2018.csv')
p_null = (len(df) - df.count()) * 100.0 / len(df)
p_null
train = df[['date', 's... | code |
34124852/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/gun-violence-data/gun-violence-data_01-2013_03-2018.csv')
p_null = (len(df) - df.count()) * 100.0 / len(df)
p_null | code |
73064431/cell_4 | [
"text_plain_output_1.png"
] | import json
output_file = open('data.txt', 'w')
output_file.write('Hello World!')
output_file.write('\n')
output_file.write('Goodbye cruel world...')
output_file.write('\n')
output_file.close()
line_list = [['ID', 'NAME', 'PRICE', 'DESCRIPTION', 'PHOTO_URL'], ['1003', 'Meat Lovers', '39.99', 'All the meats!!!', 'http... | code |
73064431/cell_3 | [
"text_plain_output_1.png"
] | output_file = open('data.txt', 'w')
output_file.write('Hello World!')
output_file.write('\n')
output_file.write('Goodbye cruel world...')
output_file.write('\n')
output_file.close()
line_list = [['ID', 'NAME', 'PRICE', 'DESCRIPTION', 'PHOTO_URL'], ['1003', 'Meat Lovers', '39.99', 'All the meats!!!', 'http://www.exampl... | code |
17105701/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
s... | code |
17105701/cell_13 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, ... | code |
17105701/cell_9 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, ... | code |
17105701/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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, ... | code |
17105701/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr | code |
17105701/cell_23 | [
"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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, ... | code |
17105701/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 # visualization tool
happy = pd.read_csv('../input/world-happiness-report-2019.csv')
happy.corr
#correlation map
f,ax = plt.subplots(figsize=(12, 12))
sns.heatmap(happy.corr(), annot=True, ... | code |
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