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stringlengths 13
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|---|---|---|---|
90153288/cell_20 | [
"text_html_output_1.png"
] | from sklearn import linear_model
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('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popularity', 'tempo', 'instrumentalness']])
df_mr.tempo = df_mr.tempo.apply(lambda row: str(row).replace('?', '0') if row == '?' else str(row))
df_mr.tempo = df_mr.tempo.apply(pd.to_numeric, errors='coerce')
df_mr = df_mr.dropna()
x = df_mr[['instrumentalness', 'tempo']]
y = df_mr['popularity']
from sklearn import linear_model
regr = linear_model.LinearRegression()
regr.fit(x, y) | code |
90153288/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 |
90153288/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)
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
for col in df.columns:
print(f'{col}:', df.dtypes[col]) | code |
90153288/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
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
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popularity', 'tempo', 'instrumentalness']])
df_mr.tempo = df_mr.tempo.apply(lambda row: str(row).replace('?', '0') if row == '?' else str(row))
df_mr.tempo = df_mr.tempo.apply(pd.to_numeric, errors='coerce')
df_mr = df_mr.dropna()
x = df_mr[['instrumentalness', 'tempo']]
y = df_mr['popularity']
from sklearn import linear_model
regr = linear_model.LinearRegression()
regr.fit(x, y)
def fit(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df_mr[['tempo', 'instrumentalness']]
y = df_mr['popularity']
f = fit(x, y)
x = df_mr[['tempo']]
y = df_mr['popularity']
def find_theta(X, y):
m = X.shape[0]
X = np.append(X, np.ones((m,1)), axis=1)
theta = np.dot(np.linalg.inv(np.dot(X.T, X)), np.dot(X.T, y))
return theta
def predict(X):
X = np.append(X, np.ones((X.shape[0],1)), axis=1)
preds = np.dot(X, theta)
return preds
theta = find_theta(x, y)
print(theta)
preds = predict(x)
fig = plt.figure(figsize=(8,6))
plt.plot(x, y, 'y.')
plt.plot(x, preds, 'c-')
plt.xlabel('Input')
plt.ylabel('target')
x = df_mr[['tempo', 'instrumentalness']]
y = df_mr['popularity']
regr2 = linear_model.LinearRegression()
regr2.fit(x.values, y)
regr2.predict([[2, 0.555]]) | code |
90153288/cell_28 | [
"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('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popularity', 'tempo', 'instrumentalness']])
df_mr.tempo = df_mr.tempo.apply(lambda row: str(row).replace('?', '0') if row == '?' else str(row))
df_mr.tempo = df_mr.tempo.apply(pd.to_numeric, errors='coerce')
df_mr = df_mr.dropna()
x = df_mr[['instrumentalness', 'tempo']]
y = df_mr['popularity']
def fit(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df_mr[['tempo', 'instrumentalness']]
y = df_mr['popularity']
f = fit(x, y)
plt.scatter(x, f)
plt.plot(x, f)
plt.xlabel('X')
plt.ylabel('f(X)') | code |
90153288/cell_16 | [
"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
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popularity', 'tempo', 'instrumentalness']])
df_mr.head(100) | code |
90153288/cell_17 | [
"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
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popularity', 'tempo', 'instrumentalness']])
df_mr.tempo = df_mr.tempo.apply(lambda row: str(row).replace('?', '0') if row == '?' else str(row))
df_mr.tempo = df_mr.tempo.apply(pd.to_numeric, errors='coerce')
df_mr = df_mr.dropna()
print(df_mr.dtypes.tempo)
df_mr.head(100) | code |
90153288/cell_10 | [
"text_html_output_1.png",
"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('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=[10, 5])
sns.barplot(x=df.music_genre, y=df.popularity) | code |
90153288/cell_12 | [
"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('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.lmplot(x='speechiness', y='popularity', data=df, ci=None, scatter=False) | code |
90153288/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
print(df.shape)
df.head() | code |
130009806/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
import plotly.express as px
# create a DataFrame with the count of farmers markets by state
state_farms = pd.DataFrame(fm['State'].value_counts())
# rename the column 'State' to 'Number of farmers markets'
state_farms.rename(columns={'State':'Number of farmers markets'}, inplace = True)
# create a bar chart
fig = px.bar(
state_farms,
x=state_farms.index,
y='Number of farmers markets',
color='Number of farmers markets', # Assign colors based on the number of farmers markets
color_continuous_scale='Blues', # Set the color scale
title='Number of Farmers Markets by State'
)
# change visual style
fig.update_layout(
xaxis_title='State',
yaxis_title='Number of Farmers Markets',
plot_bgcolor='white', # Set the background color of the plot
showlegend=False # Hide the legend
)
# show the bar chart
fig.show()
import plotly.graph_objects as go
fig = go.Figure(data=go.Scattergeo(lon=fm['x'], lat=fm['y'], mode='markers', marker=dict(size=1, opacity=1, reversescale=True, autocolorscale=False, symbol=0, line=dict(width=1, color='rgba(102, 102, 102)'), colorscale='icefire', cmin=0)))
fig.update_layout(title='US farmers markets', geo_scope='usa')
categories = ['Coffee', 'Beans', 'Fruits', 'Grains', 'Juices', 'Mushrooms', 'PetFood', 'Tofu', 'WildHarvested']
category_counts = fm[categories].eq('Y').sum()
df_category_totals = pd.DataFrame({'Category': category_counts.index, 'Count': category_counts.values})
df_category_totals = df_category_totals.sort_values('Count', ascending=False)
fig = px.bar(df_category_totals, x='Category', y='Count', color='Category', title='Frequency of Categories in Markets', labels={'Category': 'Category', 'Count': 'Count'}, color_discrete_sequence=px.colors.qualitative.Safe)
fig.show() | code |
130009806/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)
import plotly.express as px
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
import plotly.express as px
state_farms = pd.DataFrame(fm['State'].value_counts())
state_farms.rename(columns={'State': 'Number of farmers markets'}, inplace=True)
fig = px.bar(state_farms, x=state_farms.index, y='Number of farmers markets', color='Number of farmers markets', color_continuous_scale='Blues', title='Number of Farmers Markets by State')
fig.update_layout(xaxis_title='State', yaxis_title='Number of Farmers Markets', plot_bgcolor='white', showlegend=False)
fig.show() | code |
130009806/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
import plotly.express as px
# create a DataFrame with the count of farmers markets by state
state_farms = pd.DataFrame(fm['State'].value_counts())
# rename the column 'State' to 'Number of farmers markets'
state_farms.rename(columns={'State':'Number of farmers markets'}, inplace = True)
# create a bar chart
fig = px.bar(
state_farms,
x=state_farms.index,
y='Number of farmers markets',
color='Number of farmers markets', # Assign colors based on the number of farmers markets
color_continuous_scale='Blues', # Set the color scale
title='Number of Farmers Markets by State'
)
# change visual style
fig.update_layout(
xaxis_title='State',
yaxis_title='Number of Farmers Markets',
plot_bgcolor='white', # Set the background color of the plot
showlegend=False # Hide the legend
)
# show the bar chart
fig.show()
import plotly.graph_objects as go
fig = go.Figure(data=go.Scattergeo(lon=fm['x'], lat=fm['y'], mode='markers', marker=dict(size=1, opacity=1, reversescale=True, autocolorscale=False, symbol=0, line=dict(width=1, color='rgba(102, 102, 102)'), colorscale='icefire', cmin=0)))
fig.update_layout(title='US farmers markets', geo_scope='usa')
# create a collection of strings
categories = ['Coffee', 'Beans', 'Fruits', 'Grains', 'Juices', 'Mushrooms', 'PetFood', 'Tofu', 'WildHarvested']
# count the occurrences of "Y"(yes) for each category to see how many markets have those categories
category_counts = fm[categories].eq('Y').sum()
# convert the category totals to a DataFrame for plotting
df_category_totals = pd.DataFrame({'Category': category_counts.index, 'Count': category_counts.values})
# sort the DataFrame by count in descending order
df_category_totals = df_category_totals.sort_values('Count', ascending=False)
# plot the bar chart
fig = px.bar(df_category_totals, x='Category', y='Count', color='Category',
title='Frequency of Categories in Markets',
labels={'Category': 'Category', 'Count': 'Count'},
color_discrete_sequence=px.colors.qualitative.Safe)
fig.show()
def convert_dollar_number(data):
if type(data) == float:
return data
elif type(data) == str and '$' in data:
return int(''.join(data.split('$')[1].split(',')))
elif type(data) == str and '$' not in data:
return int(''.join(data.split(',')))
for i in range(3, 8):
ci[ci.columns[i]] = ci[ci.columns[i]].apply(convert_dollar_number)
state_level = ci[['State', 'median household income', 'population', 'number of households']].groupby('State').agg({'median household income': 'mean', 'population': 'mean', 'number of households': 'mean'})
markets = pd.concat([state_level, no_markets_per_state], axis=1)
markets.head() | code |
130009806/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
def convert_dollar_number(data):
if type(data) == float:
return data
elif type(data) == str and '$' in data:
return int(''.join(data.split('$')[1].split(',')))
elif type(data) == str and '$' not in data:
return int(''.join(data.split(',')))
for i in range(3, 8):
ci[ci.columns[i]] = ci[ci.columns[i]].apply(convert_dollar_number)
print('\n{:^30}'.format('Change Data Format'))
print('*' * 120) | code |
130009806/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)
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
fm.head() | code |
130009806/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 |
130009806/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
import plotly.express as px
# create a DataFrame with the count of farmers markets by state
state_farms = pd.DataFrame(fm['State'].value_counts())
# rename the column 'State' to 'Number of farmers markets'
state_farms.rename(columns={'State':'Number of farmers markets'}, inplace = True)
# create a bar chart
fig = px.bar(
state_farms,
x=state_farms.index,
y='Number of farmers markets',
color='Number of farmers markets', # Assign colors based on the number of farmers markets
color_continuous_scale='Blues', # Set the color scale
title='Number of Farmers Markets by State'
)
# change visual style
fig.update_layout(
xaxis_title='State',
yaxis_title='Number of Farmers Markets',
plot_bgcolor='white', # Set the background color of the plot
showlegend=False # Hide the legend
)
# show the bar chart
fig.show()
import plotly.graph_objects as go
fig = go.Figure(data=go.Scattergeo(lon=fm['x'], lat=fm['y'], mode='markers', marker=dict(size=1, opacity=1, reversescale=True, autocolorscale=False, symbol=0, line=dict(width=1, color='rgba(102, 102, 102)'), colorscale='icefire', cmin=0)))
fig.update_layout(title='US farmers markets', geo_scope='usa')
# create a collection of strings
categories = ['Coffee', 'Beans', 'Fruits', 'Grains', 'Juices', 'Mushrooms', 'PetFood', 'Tofu', 'WildHarvested']
# count the occurrences of "Y"(yes) for each category to see how many markets have those categories
category_counts = fm[categories].eq('Y').sum()
# convert the category totals to a DataFrame for plotting
df_category_totals = pd.DataFrame({'Category': category_counts.index, 'Count': category_counts.values})
# sort the DataFrame by count in descending order
df_category_totals = df_category_totals.sort_values('Count', ascending=False)
# plot the bar chart
fig = px.bar(df_category_totals, x='Category', y='Count', color='Category',
title='Frequency of Categories in Markets',
labels={'Category': 'Category', 'Count': 'Count'},
color_discrete_sequence=px.colors.qualitative.Safe)
fig.show()
def convert_dollar_number(data):
if type(data) == float:
return data
elif type(data) == str and '$' in data:
return int(''.join(data.split('$')[1].split(',')))
elif type(data) == str and '$' not in data:
return int(''.join(data.split(',')))
for i in range(3, 8):
ci[ci.columns[i]] = ci[ci.columns[i]].apply(convert_dollar_number)
state_level = ci[['State', 'median household income', 'population', 'number of households']].groupby('State').agg({'median household income': 'mean', 'population': 'mean', 'number of households': 'mean'})
markets = pd.concat([state_level, no_markets_per_state], axis=1)
mean_population = markets['population'].mean()
markets['population'].fillna(mean_population, inplace=True)
fig = px.scatter(data_frame=markets, x='median household income', y='no. farmers markets', size='population', color='population', color_continuous_scale='Greens', trendline='ols', trendline_color_override='red', hover_data=markets.columns, title='Relationship between median household income and no. farmers markets.')
fig.show() | code |
130009806/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
import plotly.express as px
# create a DataFrame with the count of farmers markets by state
state_farms = pd.DataFrame(fm['State'].value_counts())
# rename the column 'State' to 'Number of farmers markets'
state_farms.rename(columns={'State':'Number of farmers markets'}, inplace = True)
# create a bar chart
fig = px.bar(
state_farms,
x=state_farms.index,
y='Number of farmers markets',
color='Number of farmers markets', # Assign colors based on the number of farmers markets
color_continuous_scale='Blues', # Set the color scale
title='Number of Farmers Markets by State'
)
# change visual style
fig.update_layout(
xaxis_title='State',
yaxis_title='Number of Farmers Markets',
plot_bgcolor='white', # Set the background color of the plot
showlegend=False # Hide the legend
)
# show the bar chart
fig.show()
import plotly.graph_objects as go
fig = go.Figure(data=go.Scattergeo(lon=fm['x'], lat=fm['y'], mode='markers', marker=dict(size=1, opacity=1, reversescale=True, autocolorscale=False, symbol=0, line=dict(width=1, color='rgba(102, 102, 102)'), colorscale='icefire', cmin=0)))
fig.update_layout(title='US farmers markets', geo_scope='usa')
fig.show() | code |
130009806/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
ci.head() | code |
130009806/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
import plotly.express as px
# create a DataFrame with the count of farmers markets by state
state_farms = pd.DataFrame(fm['State'].value_counts())
# rename the column 'State' to 'Number of farmers markets'
state_farms.rename(columns={'State':'Number of farmers markets'}, inplace = True)
# create a bar chart
fig = px.bar(
state_farms,
x=state_farms.index,
y='Number of farmers markets',
color='Number of farmers markets', # Assign colors based on the number of farmers markets
color_continuous_scale='Blues', # Set the color scale
title='Number of Farmers Markets by State'
)
# change visual style
fig.update_layout(
xaxis_title='State',
yaxis_title='Number of Farmers Markets',
plot_bgcolor='white', # Set the background color of the plot
showlegend=False # Hide the legend
)
# show the bar chart
fig.show()
import plotly.graph_objects as go
fig = go.Figure(data=go.Scattergeo(lon=fm['x'], lat=fm['y'], mode='markers', marker=dict(size=1, opacity=1, reversescale=True, autocolorscale=False, symbol=0, line=dict(width=1, color='rgba(102, 102, 102)'), colorscale='icefire', cmin=0)))
fig.update_layout(title='US farmers markets', geo_scope='usa')
# create a collection of strings
categories = ['Coffee', 'Beans', 'Fruits', 'Grains', 'Juices', 'Mushrooms', 'PetFood', 'Tofu', 'WildHarvested']
# count the occurrences of "Y"(yes) for each category to see how many markets have those categories
category_counts = fm[categories].eq('Y').sum()
# convert the category totals to a DataFrame for plotting
df_category_totals = pd.DataFrame({'Category': category_counts.index, 'Count': category_counts.values})
# sort the DataFrame by count in descending order
df_category_totals = df_category_totals.sort_values('Count', ascending=False)
# plot the bar chart
fig = px.bar(df_category_totals, x='Category', y='Count', color='Category',
title='Frequency of Categories in Markets',
labels={'Category': 'Category', 'Count': 'Count'},
color_discrete_sequence=px.colors.qualitative.Safe)
fig.show()
def convert_dollar_number(data):
if type(data) == float:
return data
elif type(data) == str and '$' in data:
return int(''.join(data.split('$')[1].split(',')))
elif type(data) == str and '$' not in data:
return int(''.join(data.split(',')))
for i in range(3, 8):
ci[ci.columns[i]] = ci[ci.columns[i]].apply(convert_dollar_number)
state_level = ci[['State', 'median household income', 'population', 'number of households']].groupby('State').agg({'median household income': 'mean', 'population': 'mean', 'number of households': 'mean'})
markets = pd.concat([state_level, no_markets_per_state], axis=1)
# replace NaN values in 'population' column with 0
mean_population = markets['population'].mean()
markets['population'].fillna(mean_population, inplace=True)
# plot the scatter plot
fig = px.scatter(
data_frame=markets,
x='median household income',
y='no. farmers markets',
size='population',
color='population',
color_continuous_scale='Greens',
trendline='ols',
trendline_color_override='red',
hover_data=markets.columns,
title='Relationship between median household income and no. farmers markets.'
)
fig.show()
markets['median household income'].fillna(0, inplace=True)
fig = px.scatter(data_frame=markets, x='population', y='no. farmers markets', trendline='ols', size='median household income', color='median household income', color_continuous_scale='Blues', hover_data=markets.columns, title='Relationship between population and no. farmers markets per state.')
fig.show() | code |
1006479/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
df_store.head() | code |
1006479/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv') | code |
1006479/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.head() | code |
73075563/cell_13 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import matthews_corrcoef
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model.fit(X_train, y_train)
print(matthews_corrcoef(y_test, model.predict(X_test)))
print(accuracy_score(y_test, model.predict(X_test))) | code |
73075563/cell_9 | [
"text_plain_output_1.png"
] | import hypertools as hyp
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.groupby('DEATH_EVENT').size()
labels = data['DEATH_EVENT']
hyp.plot(data, '.', hue=labels, reduce='PCA', normalize='within', legend=labels.unique().tolist()) | code |
73075563/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.head() | code |
73075563/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.describe() | code |
73075563/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 |
73075563/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.groupby('DEATH_EVENT').size() | code |
73075563/cell_18 | [
"text_plain_output_1.png"
] | from keras import callbacks
from keras.layers import Dense, BatchNormalization, Dropout, LSTM
from keras.models import Sequential
from sklearn.metrics import accuracy_score
from sklearn.metrics import matthews_corrcoef
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import numpy as np # linear algebra
import torch
import torch
import torch.nn as nn
model = DecisionTreeClassifier(random_state=0)
model.fit(X_train, y_train)
from keras.layers import Dense, BatchNormalization, Dropout, LSTM
from keras.models import Sequential
from keras.utils import to_categorical
from keras import callbacks
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
early_stopping = callbacks.EarlyStopping(min_delta=0.001, patience=20, restore_best_weights=True)
model = Sequential()
model.add(Dense(units=9, kernel_initializer='uniform', activation='relu', input_dim=12))
model.add(Dense(units=9, kernel_initializer='uniform', activation='relu'))
model.add(Dense(units=7, kernel_initializer='uniform', activation='relu'))
model.add(Dense(units=5, kernel_initializer='uniform', activation='relu'))
model.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, batch_size=32, epochs=500, validation_split=0.2, callbacks=[early_stopping])
val_accuracy = np.mean(history.history['val_accuracy'])
import torch
import torch.nn as nn
class HeartFailureSimpleNNModel(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(12, 9)
self.ReLU1 = nn.ReLU()
self.linear2 = nn.Linear(9, 9)
self.ReLU2 = nn.ReLU()
self.linear3 = nn.Linear(9, 7)
self.ReLU3 = nn.ReLU()
self.linear4 = nn.Linear(7, 5)
self.ReLU4 = nn.ReLU()
self.linear5 = nn.Linear(5, 1)
def forward(self, x):
lin1_out = self.linear1(x)
ReLU_out1 = self.ReLU1(lin1_out)
ReLU_out2 = self.ReLU2(self.linear2(ReLU_out1))
ReLU_out3 = self.ReLU3(self.linear3(ReLU_out2))
ReLU_out4 = self.ReLU4(self.linear4(ReLU_out3))
return self.linear5(ReLU_out4).sigmoid_()
net = HeartFailureSimpleNNModel()
criterion = nn.BCELoss()
num_epochs = 500
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
input_tensor = torch.from_numpy(X_train_2).type(torch.FloatTensor)
label_tensor = torch.from_numpy(y_train_2)
label_tensor = label_tensor.float()
for epoch in range(num_epochs):
output = net(input_tensor.float())
loss = criterion(output, label_tensor.unsqueeze(1))
optimizer.zero_grad()
loss.backward()
optimizer.step()
validation_tensor = torch.from_numpy(X_val).type(torch.FloatTensor)
print(input_tensor)
print(validation_tensor)
out_probs = net(input_tensor).detach().numpy()
print(out_probs)
out_classes = np.round(out_probs)
print(out_classes)
print(label_tensor)
validation_out_probs = net(validation_tensor).detach().numpy()
print(validation_out_probs) | code |
73075563/cell_8 | [
"text_plain_output_1.png"
] | import hypertools as hyp
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.groupby('DEATH_EVENT').size()
labels = data['DEATH_EVENT']
hyp.plot(data, '.', hue=labels, reduce='PCA', legend=labels.unique().tolist()) | code |
73075563/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import sklearn
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import hypertools as hyp
import seaborn as sns
import numpy as np
import torch
!pip install captum
from captum.attr import IntegratedGradients | code |
73075563/cell_17 | [
"text_html_output_1.png"
] | import torch
import torch
import torch.nn as nn
import torch
import torch.nn as nn
class HeartFailureSimpleNNModel(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(12, 9)
self.ReLU1 = nn.ReLU()
self.linear2 = nn.Linear(9, 9)
self.ReLU2 = nn.ReLU()
self.linear3 = nn.Linear(9, 7)
self.ReLU3 = nn.ReLU()
self.linear4 = nn.Linear(7, 5)
self.ReLU4 = nn.ReLU()
self.linear5 = nn.Linear(5, 1)
def forward(self, x):
lin1_out = self.linear1(x)
ReLU_out1 = self.ReLU1(lin1_out)
ReLU_out2 = self.ReLU2(self.linear2(ReLU_out1))
ReLU_out3 = self.ReLU3(self.linear3(ReLU_out2))
ReLU_out4 = self.ReLU4(self.linear4(ReLU_out3))
return self.linear5(ReLU_out4).sigmoid_()
net = HeartFailureSimpleNNModel()
criterion = nn.BCELoss()
num_epochs = 500
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
input_tensor = torch.from_numpy(X_train_2).type(torch.FloatTensor)
label_tensor = torch.from_numpy(y_train_2)
label_tensor = label_tensor.float()
for epoch in range(num_epochs):
output = net(input_tensor.float())
loss = criterion(output, label_tensor.unsqueeze(1))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % 20 == 0:
print('Epoch {}/{} => Loss: {:.2f}'.format(epoch + 1, num_epochs, loss.item())) | code |
73075563/cell_14 | [
"text_plain_output_1.png"
] | from keras import callbacks
from keras.layers import Dense, BatchNormalization, Dropout, LSTM
from keras.models import Sequential
from sklearn.metrics import accuracy_score
from sklearn.metrics import matthews_corrcoef
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import numpy as np # linear algebra
model = DecisionTreeClassifier(random_state=0)
model.fit(X_train, y_train)
from keras.layers import Dense, BatchNormalization, Dropout, LSTM
from keras.models import Sequential
from keras.utils import to_categorical
from keras import callbacks
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
early_stopping = callbacks.EarlyStopping(min_delta=0.001, patience=20, restore_best_weights=True)
model = Sequential()
model.add(Dense(units=9, kernel_initializer='uniform', activation='relu', input_dim=12))
model.add(Dense(units=9, kernel_initializer='uniform', activation='relu'))
model.add(Dense(units=7, kernel_initializer='uniform', activation='relu'))
model.add(Dense(units=5, kernel_initializer='uniform', activation='relu'))
model.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, batch_size=32, epochs=500, validation_split=0.2, callbacks=[early_stopping])
val_accuracy = np.mean(history.history['val_accuracy'])
print('\n%s: %.2f%%' % ('val_accuracy', val_accuracy * 100)) | code |
73075563/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.groupby('DEATH_EVENT').size()
corr = data.corr()
sns.heatmap(corr) | code |
73075563/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)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum() | code |
105206902/cell_42 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
from sklearn.ensemble import RandomForestClassifier
rfcfl = RandomForestClassifier(random_state=0)
rfcfl.fit(X_train, y_train)
rfcfl.score(X_test, y_test) | code |
105206902/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
dftotal['attribute_0'].value_counts() | code |
105206902/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.describe() | code |
105206902/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
dftotal['product_code'].value_counts() | code |
105206902/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftest.head() | code |
105206902/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
f = plt.figure(figsize=(20, 10))
sns.heatmap(dftotal.corr());
corrtable = dftotal.corr()
print('the minimum value for loading for failure cases is {} and maximum value is {}'.format(dftotal[dftotal['failure'] == 1]['loading'].min(), dftotal[dftotal['failure'] == 1]['loading'].max()))
print('the minimum value for loading for failure cases is {} and maximum value is {}'.format(dftotal[dftotal['failure'] == 0]['loading'].min(), dftotal[dftotal['failure'] == 0]['loading'].max())) | code |
105206902/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
dftotal['attribute_1'].value_counts() | code |
105206902/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
from sklearn.svm import SVC
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)
y_pred = svclassifier.predict(X_test)
svclassifier.score(X_test, y_test) | code |
105206902/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
print(dftrain.shape)
print(dftest.shape)
print(dftotal.shape) | code |
105206902/cell_40 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
from sklearn.linear_model import LogisticRegression
lrclf = LogisticRegression(random_state=0)
lrclf.fit(X_train, y_train)
y_predict = lrclf.predict(X_test)
lrclf.score(X_test, y_test) | code |
105206902/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
f = plt.figure(figsize=(20, 10))
sns.heatmap(dftotal.corr());
corrtable = dftotal.corr()
corrtable.head() | code |
105206902/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
dftotal['product_code'] = labelencoder.fit_transform(dftotal['product_code'])
dftotal['product_code'].value_counts() | code |
105206902/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
f = plt.figure(figsize=(20, 10))
sns.heatmap(dftotal.corr());
corrtable = dftotal.corr()
import numpy as np
from sklearn.model_selection import train_test_split
dftrainset = dftotal[~dftotal['failure'].isnull()]
dftestset = dftotal[dftotal['failure'].isnull()]
X = dftrainset[['id', 'product_code', 'loading', 'attribute_0', 'attribute_1', 'attribute_2', 'attribute_3', 'measurement_0', 'measurement_1', 'measurement_2', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']]
y = dftrainset['failure']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
from sklearn.decomposition import PCA
pca = PCA(n_components=4)
X = dftotal[['loading', 'attribute_0', 'attribute_1', 'attribute_2', 'attribute_3', 'measurement_0', 'measurement_1', 'measurement_2', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']]
y = dftotal['failure']
principalComponents = pca.fit_transform(X)
principalDf = pd.DataFrame(data=principalComponents, columns=['principal component 1', 'principal component 2', 'principal component 3', 'principal component 4'])
dfpcatotal = pd.concat([principalDf, dftrainset[['failure', 'id', 'product_code']]], axis=1)
dfpcatotal.head() | code |
105206902/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
print(dftotal.shape) | code |
105206902/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.info() | code |
105206902/cell_45 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
f = plt.figure(figsize=(20, 10))
sns.heatmap(dftotal.corr());
corrtable = dftotal.corr()
dftotal.head() | code |
105206902/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
f = plt.figure(figsize=(20, 10))
sns.heatmap(dftotal.corr()) | code |
105206902/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum() | code |
105206902/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal.head() | code |
105206902/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftrain.head() | code |
105206902/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal = dftotal[dftotal['loading'].notna()]
f = plt.figure(figsize=(20, 10))
sns.heatmap(dftotal.corr());
corrtable = dftotal.corr()
irow, icol = corrtable.shape
for row in range(0, irow):
for col in range(0, icol):
corval = corrtable.iloc[row, col]
if corval > 0.4:
if corrtable.index[row] != corrtable.columns[col]:
print('correlation with {} and {} is {}'.format(corrtable.index[row], corrtable.columns[col], corrtable.iloc[row, col])) | code |
105206902/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns | code |
105206902/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum() | code |
105206902/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.head() | code |
89132929/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
lista['price'] = lista['price'].str.lstrip('$')
lista['price'] = lista['price'].str.replace(',', '')
lista['price'] = pd.to_numeric(lista['price'], downcast='float')
lista['host_since'] = pd.to_datetime(lista['host_since'])
calendar['price'] = calendar['price'].str.lstrip('$')
calendar['price'] = calendar['price'].str.replace(',', '')
calendar['price'] = pd.to_numeric(calendar['price'], downcast='float')
calendar['adjusted_price'] = calendar['adjusted_price'].str.lstrip('$')
calendar['adjusted_price'] = calendar['adjusted_price'].str.replace(',', '')
calendar['adjusted_price'] = pd.to_numeric(calendar['adjusted_price'], downcast='float')
bins = range(0, 400, 20)
plt.plot
bins = range(0, 370, 20)
plt.plot
bins = range(0, 1000, 100)
plt.plot
plt.hist(lista['room_type'], histtype='bar', rwidth=1.2, color='grey')
plt.xlabel('Tipo de Acomodação')
plt.ylabel('Contagem')
plt.plot
plt.show() | code |
89132929/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
precmax = max(lista['price'])
precmin = min(lista['price'])
precmed = np.mean(lista['price'])
print(f'O preço máximo é {precmax}. O preço mínimo é {precmin}. O preço médio é {precmed}') | code |
89132929/cell_6 | [
"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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
reviews.head(n=6) | code |
89132929/cell_11 | [
"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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
Tipos = lista['room_type'].unique()
print(Tipos) | code |
89132929/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import folium
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 |
89132929/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
lista['price'] = lista['price'].str.lstrip('$')
lista['price'] = lista['price'].str.replace(',', '')
lista['price'] = pd.to_numeric(lista['price'], downcast='float')
lista['host_since'] = pd.to_datetime(lista['host_since'])
calendar['price'] = calendar['price'].str.lstrip('$')
calendar['price'] = calendar['price'].str.replace(',', '')
calendar['price'] = pd.to_numeric(calendar['price'], downcast='float')
calendar['adjusted_price'] = calendar['adjusted_price'].str.lstrip('$')
calendar['adjusted_price'] = calendar['adjusted_price'].str.replace(',', '')
calendar['adjusted_price'] = pd.to_numeric(calendar['adjusted_price'], downcast='float')
bins = range(0, 400, 20)
plt.hist(lista['minimum_nights'], bins, histtype='bar', rwidth=1.2)
plt.xlabel('Mínimas noites')
plt.ylabel('Contagem')
plt.plot
plt.axvline(calendar['minimum_nights'].mean(), linestyle='dashed', color='red')
plt.show() | code |
89132929/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
lista['price'] = lista['price'].str.lstrip('$')
lista['price'] = lista['price'].str.replace(',', '')
lista['price'] = pd.to_numeric(lista['price'], downcast='float')
lista['host_since'] = pd.to_datetime(lista['host_since'])
calendar['price'] = calendar['price'].str.lstrip('$')
calendar['price'] = calendar['price'].str.replace(',', '')
calendar['price'] = pd.to_numeric(calendar['price'], downcast='float')
calendar['adjusted_price'] = calendar['adjusted_price'].str.lstrip('$')
calendar['adjusted_price'] = calendar['adjusted_price'].str.replace(',', '')
calendar['adjusted_price'] = pd.to_numeric(calendar['adjusted_price'], downcast='float')
bins = range(0, 400, 20)
plt.plot
bins = range(0, 370, 20)
plt.hist(lista['availability_365'], bins, histtype='bar', rwidth=1.2, color='green')
plt.xlabel('Disponibilidade em 365 dias')
plt.ylabel('Contagem')
plt.plot
plt.axvline(lista['availability_365'].mean(), linestyle='dashed', color='red')
plt.show() | code |
89132929/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
lista['price'] = lista['price'].str.lstrip('$')
lista['price'] = lista['price'].str.replace(',', '')
lista['price'] = pd.to_numeric(lista['price'], downcast='float')
lista['price'].head(6) | code |
89132929/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
lista['price'] = lista['price'].str.lstrip('$')
lista['price'] = lista['price'].str.replace(',', '')
lista['price'] = pd.to_numeric(lista['price'], downcast='float')
lista['host_since'] = pd.to_datetime(lista['host_since'])
calendar['price'] = calendar['price'].str.lstrip('$')
calendar['price'] = calendar['price'].str.replace(',', '')
calendar['price'] = pd.to_numeric(calendar['price'], downcast='float')
calendar['adjusted_price'] = calendar['adjusted_price'].str.lstrip('$')
calendar['adjusted_price'] = calendar['adjusted_price'].str.replace(',', '')
calendar['adjusted_price'] = pd.to_numeric(calendar['adjusted_price'], downcast='float')
bins = range(0, 400, 20)
plt.plot
bins = range(0, 370, 20)
plt.plot
bins = range(0, 1000, 100)
plt.plot
plt.plot
plt.scatter(lista['room_type'], lista['price'])
plt.xlabel('Tipo de Acomodação')
plt.ylabel('Preço')
plt.plot
plt.show() | code |
89132929/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
lista['price'] = lista['price'].str.lstrip('$')
lista['price'] = lista['price'].str.replace(',', '')
lista['price'] = pd.to_numeric(lista['price'], downcast='float')
lista['host_since'] = pd.to_datetime(lista['host_since'])
calendar['price'] = calendar['price'].str.lstrip('$')
calendar['price'] = calendar['price'].str.replace(',', '')
calendar['price'] = pd.to_numeric(calendar['price'], downcast='float')
calendar['adjusted_price'] = calendar['adjusted_price'].str.lstrip('$')
calendar['adjusted_price'] = calendar['adjusted_price'].str.replace(',', '')
calendar['adjusted_price'] = pd.to_numeric(calendar['adjusted_price'], downcast='float')
bins = range(0, 400, 20)
plt.plot
bins = range(0, 370, 20)
plt.plot
bins = range(0, 1000, 100)
plt.hist(lista['price'], bins, histtype='bar', rwidth=100, color='yellow')
plt.xlabel('Preços')
plt.ylabel('Contagem')
plt.plot
plt.axvline(lista['price'].mean(), linestyle='dashed', color='blue')
plt.show() | code |
89132929/cell_12 | [
"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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
Tipos_Propriedades = lista['property_type'].unique()
print(Tipos_Propriedades) | code |
89132929/cell_5 | [
"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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/kaggle/input/mapamelbourne/Melbourne.png')
lista['price'] = lista['price'].str.lstrip('$')
lista['price'] = lista['price'].str.replace(',', '')
lista['price'] = pd.to_numeric(lista['price'], downcast='float')
lista['host_since'] = pd.to_datetime(lista['host_since'])
calendar['price'] = calendar['price'].str.lstrip('$')
calendar['price'] = calendar['price'].str.replace(',', '')
calendar['price'] = pd.to_numeric(calendar['price'], downcast='float')
calendar['adjusted_price'] = calendar['adjusted_price'].str.lstrip('$')
calendar['adjusted_price'] = calendar['adjusted_price'].str.replace(',', '')
calendar['adjusted_price'] = pd.to_numeric(calendar['adjusted_price'], downcast='float')
calendar.head(n=6) | code |
122249887/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
useless_cols = ['PassengerId', 'Name', 'Cabin']
df.drop(columns=useless_cols, inplace=True)
import seaborn as sns
corr_matrix = df.corr()
high_corr = set()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if abs(corr_matrix.iloc[i, j]) > 0.7:
colname = corr_matrix.columns[i]
high_corr.add(colname)
print(high_corr) | code |
122249887/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
useless_cols = ['PassengerId', 'Name', 'Cabin']
df.drop(columns=useless_cols, inplace=True)
import seaborn as sns
corr_matrix = df.corr()
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
X = df.drop(columns=['Transported'])
y = df.Transported
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
numerical_cols = [cname for cname in X.columns if X[cname].dtype in ['int64', 'float64']]
numerical_transformer = Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())])
categorical_cols = [cname for cname in X.columns if X[cname].nunique() < 10 and X[cname].dtype == 'object']
categorical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='most_frequent')), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(transformers=[('num', numerical_transformer, numerical_cols), ('cat', categorical_transformer, categorical_cols)])
pipe = Pipeline(steps=[('preprocessor', preprocessor), ('model', LogisticRegression())])
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
scores = cross_val_score(pipe, X, y, cv=5)
print('Cross-validation scores:', scores)
print('MAE:', scores.mean()) | code |
122249887/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
useless_cols = ['PassengerId', 'Name', 'Cabin']
df.drop(columns=useless_cols, inplace=True)
import seaborn as sns
corr_matrix = df.corr()
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
X = df.drop(columns=['Transported'])
y = df.Transported
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
numerical_cols = [cname for cname in X.columns if X[cname].dtype in ['int64', 'float64']]
numerical_transformer = Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())])
categorical_cols = [cname for cname in X.columns if X[cname].nunique() < 10 and X[cname].dtype == 'object']
categorical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='most_frequent')), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(transformers=[('num', numerical_transformer, numerical_cols), ('cat', categorical_transformer, categorical_cols)])
pipe = Pipeline(steps=[('preprocessor', preprocessor), ('model', LogisticRegression())])
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred)) | code |
122249887/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
useless_cols = ['PassengerId', 'Name', 'Cabin']
df.drop(columns=useless_cols, inplace=True)
import seaborn as sns
corr_matrix = df.corr()
sns.heatmap(corr_matrix, cmap='coolwarm', annot=True) | code |
130002260/cell_21 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
average_calories = df.groupby('type')['calories'].mean()
correlation = df[['calories', 'fat', 'carb', 'fiber', 'protein']].corr()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
corr_value = correlation.loc['calories', nutrient]
average_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
print('Food Item Types with the Highest Average Nutrient Content:')
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
max_nutrient_type = average_nutrients[nutrient].idxmax()
max_nutrient_value = average_nutrients[nutrient].max()
print(f'- Highest Average {nutrient.capitalize()}: {max_nutrient_type} ({max_nutrient_value:.2f} grams)') | code |
130002260/cell_13 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
print(f'Food Item with Highest Fat: {highest_fat_item} ({highest_fat_value} grams)')
print(f'Food Item with Highest Carbohydrates: {highest_carb_item} ({highest_carb_value} grams)')
print(f'Food Item with Highest Fiber: {highest_fiber_item} ({highest_fiber_value} grams)')
print(f'Food Item with Highest Protein: {highest_protein_item} ({highest_protein_value} grams)') | code |
130002260/cell_25 | [
"text_plain_output_1.png"
] | from pprint import pprint
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pprint
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
average_calories = df.groupby('type')['calories'].mean()
correlation = df[['calories', 'fat', 'carb', 'fiber', 'protein']].corr()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
corr_value = correlation.loc['calories', nutrient]
average_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
max_nutrient_type = average_nutrients[nutrient].idxmax()
max_nutrient_value = average_nutrients[nutrient].max()
mean_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
nutrient_columns = mean_nutrients.columns
for nutrient in nutrient_columns:
nutrient_values = mean_nutrients[nutrient]
plt.xticks(rotation=45)
# Calculate summary statistics for each nutrient by food item type
summary_statistics = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].describe()
# Use pprint for pretty printing
pp = pprint.PrettyPrinter()
# Print the summary statistics
print("Summary Statistics for Nutrient Values by Food Item Type:")
pp.pprint(summary_statistics)
mean_fat_by_type = df.groupby('type')['fat'].mean()
pp = pprint.PrettyPrinter()
print('Mean Fat Content by Food Item Type:')
pp.pprint(mean_fat_by_type) | code |
130002260/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)
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
average_calories = df.groupby('type')['calories'].mean()
correlation = df[['calories', 'fat', 'carb', 'fiber', 'protein']].corr()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
corr_value = correlation.loc['calories', nutrient]
average_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
max_nutrient_type = average_nutrients[nutrient].idxmax()
max_nutrient_value = average_nutrients[nutrient].max()
mean_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
nutrient_columns = mean_nutrients.columns
for nutrient in nutrient_columns:
nutrient_values = mean_nutrients[nutrient]
plt.figure()
plt.bar(nutrient_values.index, nutrient_values)
plt.xlabel('Food Item Type')
plt.ylabel('Mean ' + nutrient.capitalize() + ' (grams)')
plt.title('Mean ' + nutrient.capitalize() + ' Content by Food Item Type')
plt.xticks(rotation=45)
plt.show() | code |
130002260/cell_6 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe | code |
130002260/cell_11 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
print(f'Highest Calorie Food Item: {highest_calorie_item} ({highest_calorie_value} calories)')
print(f'Lowest Calorie Food Item: {lowest_calorie_item} ({lowest_calorie_value} calories)') | code |
130002260/cell_19 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
average_calories = df.groupby('type')['calories'].mean()
correlation = df[['calories', 'fat', 'carb', 'fiber', 'protein']].corr()
print('Correlation between Calorie Content and Nutrients:')
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
corr_value = correlation.loc['calories', nutrient]
print(f'- {nutrient.capitalize()}: {corr_value:.2f}') | code |
130002260/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from pprint import pprint
import matplotlib.pyplot as plt
import pprint
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130002260/cell_7 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.info() | code |
130002260/cell_8 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum() | code |
130002260/cell_15 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
filtered_items = df[(df['fiber'] > 0) & (df['fat'] < 5) & (df['carb'] < 30)]
if filtered_items.empty:
print('No food items are particularly rich in fiber but low in fat and carbohydrates.')
else:
print('Food items that are particularly rich in fiber but low in fat and carbohydrates:')
for item in filtered_items['item']:
print(f'- {item}') | code |
130002260/cell_17 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
average_calories = df.groupby('type')['calories'].mean()
print('Average Calorie Content by Food Item Type:')
for food_type, avg_calories in average_calories.items():
print(f'- {food_type}: {avg_calories:.2f} calories') | code |
130002260/cell_24 | [
"text_plain_output_1.png"
] | from pprint import pprint
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pprint
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
average_calories = df.groupby('type')['calories'].mean()
correlation = df[['calories', 'fat', 'carb', 'fiber', 'protein']].corr()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
corr_value = correlation.loc['calories', nutrient]
average_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
max_nutrient_type = average_nutrients[nutrient].idxmax()
max_nutrient_value = average_nutrients[nutrient].max()
mean_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
nutrient_columns = mean_nutrients.columns
for nutrient in nutrient_columns:
nutrient_values = mean_nutrients[nutrient]
plt.xticks(rotation=45)
summary_statistics = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].describe()
pp = pprint.PrettyPrinter()
print('Summary Statistics for Nutrient Values by Food Item Type:')
pp.pprint(summary_statistics) | code |
130002260/cell_27 | [
"text_plain_output_1.png"
] | from pprint import pprint
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pprint
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.loc[df['calories'].idxmin(), 'item']
lowest_calorie_value = df['calories'].min()
highest_fat_item = df.loc[df['fat'].idxmax(), 'item']
highest_fat_value = df['fat'].max()
highest_carb_item = df.loc[df['carb'].idxmax(), 'item']
highest_carb_value = df['carb'].max()
highest_fiber_item = df.loc[df['fiber'].idxmax(), 'item']
highest_fiber_value = df['fiber'].max()
highest_protein_item = df.loc[df['protein'].idxmax(), 'item']
highest_protein_value = df['protein'].max()
average_calories = df.groupby('type')['calories'].mean()
correlation = df[['calories', 'fat', 'carb', 'fiber', 'protein']].corr()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
corr_value = correlation.loc['calories', nutrient]
average_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
for nutrient in ['fat', 'carb', 'fiber', 'protein']:
max_nutrient_type = average_nutrients[nutrient].idxmax()
max_nutrient_value = average_nutrients[nutrient].max()
mean_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
nutrient_columns = mean_nutrients.columns
for nutrient in nutrient_columns:
nutrient_values = mean_nutrients[nutrient]
plt.xticks(rotation=45)
# Calculate summary statistics for each nutrient by food item type
summary_statistics = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].describe()
# Use pprint for pretty printing
pp = pprint.PrettyPrinter()
# Print the summary statistics
print("Summary Statistics for Nutrient Values by Food Item Type:")
pp.pprint(summary_statistics)
mean_fat_by_type = df.groupby('type')['fat'].mean()
pp = pprint.PrettyPrinter()
mean_nutrients = df.groupby('type')[['fat', 'carb', 'fiber', 'protein']].mean()
overall_mean_nutrients = df[['fat', 'carb', 'fiber', 'protein']].mean()
nutrient_differences = mean_nutrients.subtract(overall_mean_nutrients, axis='columns')
print('Notable Differences in Nutritional Profile across Food Item Categories:')
print(nutrient_differences) | code |
130002260/cell_5 | [
"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/starbucks-nutrition/starbucks.csv', index_col=0)
df | code |
88100087/cell_23 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import KBinsDiscretizer
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
df.dropna(inplace=True)
X = df.iloc[:, 1:]
y = df.iloc[:, 0]
kbin_age = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
kbin_fare = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
trf = ColumnTransformer([('first', kbin_age, [0]), ('second', kbin_fare, [1])])
X_train_trf = trf.fit_transform(X_train)
X_test_trf = trf.transform(X_test)
output = pd.DataFrame({'age': X_train['Age'], 'age_trf': X_train_trf[:, 0], 'fare': X_train['Fare'], 'fare_trf': X_train_trf[:, 1]})
output['age_labels'] = pd.cut(x=X_train['Age'], bins=trf.named_transformers_['first'].bin_edges_[0].tolist())
output['fare_labels'] = pd.cut(x=X_train['Fare'], bins=trf.named_transformers_['second'].bin_edges_[0].tolist())
df = pd.read_csv('/kaggle/input/titanic/train.csv')[['Age', 'Fare', 'SibSp', 'Parch', 'Survived']]
trf = ColumnTransformer([('bin', Binarizer(copy=False), ['family'])], remainder='passthrough')
X_train_trf = trf.fit_transform(X_train)
X_test_trf = trf.transform(X_test)
pd.DataFrame(X_train_trf, columns=['family', 'Age', 'Fare']) | code |
88100087/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88100087/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
df.head() | code |
88100087/cell_17 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import KBinsDiscretizer
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
df.dropna(inplace=True)
X = df.iloc[:, 1:]
y = df.iloc[:, 0]
kbin_age = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
kbin_fare = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
trf = ColumnTransformer([('first', kbin_age, [0]), ('second', kbin_fare, [1])])
X_train_trf = trf.fit_transform(X_train)
X_test_trf = trf.transform(X_test)
output = pd.DataFrame({'age': X_train['Age'], 'age_trf': X_train_trf[:, 0], 'fare': X_train['Fare'], 'fare_trf': X_train_trf[:, 1]})
output['age_labels'] = pd.cut(x=X_train['Age'], bins=trf.named_transformers_['first'].bin_edges_[0].tolist())
output['fare_labels'] = pd.cut(x=X_train['Fare'], bins=trf.named_transformers_['second'].bin_edges_[0].tolist())
df = pd.read_csv('/kaggle/input/titanic/train.csv')[['Age', 'Fare', 'SibSp', 'Parch', 'Survived']]
df.drop(columns=['SibSp', 'Parch'], inplace=True)
df.head() | code |
88100087/cell_14 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import KBinsDiscretizer
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
df.dropna(inplace=True)
X = df.iloc[:, 1:]
y = df.iloc[:, 0]
kbin_age = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
kbin_fare = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
trf = ColumnTransformer([('first', kbin_age, [0]), ('second', kbin_fare, [1])])
X_train_trf = trf.fit_transform(X_train)
X_test_trf = trf.transform(X_test)
output = pd.DataFrame({'age': X_train['Age'], 'age_trf': X_train_trf[:, 0], 'fare': X_train['Fare'], 'fare_trf': X_train_trf[:, 1]})
output['age_labels'] = pd.cut(x=X_train['Age'], bins=trf.named_transformers_['first'].bin_edges_[0].tolist())
output['fare_labels'] = pd.cut(x=X_train['Fare'], bins=trf.named_transformers_['second'].bin_edges_[0].tolist())
df = pd.read_csv('/kaggle/input/titanic/train.csv')[['Age', 'Fare', 'SibSp', 'Parch', 'Survived']]
df.head() | code |
88100087/cell_12 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import KBinsDiscretizer
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
kbin_age = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
kbin_fare = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
trf = ColumnTransformer([('first', kbin_age, [0]), ('second', kbin_fare, [1])])
X_train_trf = trf.fit_transform(X_train)
X_test_trf = trf.transform(X_test)
output = pd.DataFrame({'age': X_train['Age'], 'age_trf': X_train_trf[:, 0], 'fare': X_train['Fare'], 'fare_trf': X_train_trf[:, 1]})
output.sample(5) | code |
105194628/cell_2 | [
"text_plain_output_1.png"
] | input1 = int(input('Enter a integer'))
if input1 > 0:
print('The number is postive')
elif input1 < 0:
print('The number is not positive')
else:
print('The number is zero') | code |
105194628/cell_7 | [
"text_plain_output_1.png"
] | n1 = int(input('Enter your number 1'))
n2 = int(input('Enter your number 2'))
n3 = int(input('Enter your number 3'))
if n1 > n2 and n3:
print(n1, 'It is the maximun value')
elif n2 > n1 and n3:
print(n2, 'It is the maximun value')
elif n3 > n1 and n2:
print(n3, 'It is the maximun value') | code |
105194628/cell_8 | [
"text_plain_output_1.png"
] | n1 = int(input('Enter your number 1'))
n2 = int(input('Enter your number 2'))
n3 = int(input('Enter your number 3'))
max = n1
if max < n2:
max = n2
if max < n3:
max = n3
print(max) | code |
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