path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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|---|---|---|---|
1007485/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
data.shape
Color_Count = data.color.value_counts()
idx = range(2)
labels = ['Color', 'Black & White']
plt.xticks(idx, labels)
Director = data.director_name.value_counts()
D_Name = Director.head(n=10).index
New_D = data[(data['director_name'].isin(D_Name))]
New_D.pivot_table(index=['director_name','imdb_score'],aggfunc='mean')
plt.figure(1,figsize=(12,6))
plt.subplot(1,2,1)
Director.head(n=10).sort_index().plot(kind='bar')
plt.title('Top 10 directors that have most volume movies')
plt.subplot(1,2,2)
New_D.groupby(['director_name'])['imdb_score'].mean().plot(kind='bar')
plt.xlabel("")
plt.title("Top 10 direcotors' average IMDB scores")
plt.show()
Language = data.language.value_counts()
Language.head(n=10).plot(kind='bar')
plt.title('Top 10 movie languages')
plt.show() | code |
1007485/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
data.shape
Color_Count = data.color.value_counts()
idx = range(2)
labels = ['Color', 'Black & White']
plt.xticks(idx, labels)
Director = data.director_name.value_counts()
D_Name = Director.head(n=10).index
New_D = data[(data['director_name'].isin(D_Name))]
New_D.pivot_table(index=['director_name','imdb_score'],aggfunc='mean')
plt.figure(1,figsize=(12,6))
plt.subplot(1,2,1)
Director.head(n=10).sort_index().plot(kind='bar')
plt.title('Top 10 directors that have most volume movies')
plt.subplot(1,2,2)
New_D.groupby(['director_name'])['imdb_score'].mean().plot(kind='bar')
plt.xlabel("")
plt.title("Top 10 direcotors' average IMDB scores")
plt.show()
Language = data.language.value_counts()
Country = data.country.value_counts()
Country.head(n=10).plot(kind='barh')
plt.title('Top 10 Countries that produce movies')
plt.show() | code |
1007485/cell_3 | [
"image_output_1.png"
] | data.shape | code |
1007485/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
data.shape
Color_Count = data.color.value_counts()
idx = range(2)
labels = ['Color', 'Black & White']
plt.xticks(idx, labels)
Director = data.director_name.value_counts()
D_Name = Director.head(n=10).index
New_D = data[(data['director_name'].isin(D_Name))]
New_D.pivot_table(index=['director_name','imdb_score'],aggfunc='mean')
plt.figure(1,figsize=(12,6))
plt.subplot(1,2,1)
Director.head(n=10).sort_index().plot(kind='bar')
plt.title('Top 10 directors that have most volume movies')
plt.subplot(1,2,2)
New_D.groupby(['director_name'])['imdb_score'].mean().plot(kind='bar')
plt.xlabel("")
plt.title("Top 10 direcotors' average IMDB scores")
plt.show()
Language = data.language.value_counts()
Country = data.country.value_counts()
score_by_content = data.pivot_table(index=['content_rating'], values='imdb_score', aggfunc='mean')
Contents = data.content_rating.value_counts().sort_index()
Year = data.title_year.value_counts().sort_index().tail(50)
year = range(50)
plt.figure(1, figsize=(12, 6))
loc = range(3, 49, 5)
ticks = range(1970, 2017, 5)
plt.bar(year, Year)
plt.xticks(loc, ticks)
plt.xlabel('Year')
plt.title('Number of movies titled in recent 50 years', fontsize=15)
plt.show() | code |
1007485/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
data.shape
Color_Count = data.color.value_counts()
plt.figure(1, figsize=(6, 6))
idx = range(2)
labels = ['Color', 'Black & White']
plt.bar(idx, Color_Count, width=0.3)
plt.xticks(idx, labels)
plt.show() | code |
18104028/cell_4 | [
"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('../input/market_data_02.csv')
df.columns
fig, ax = plt.subplots(figsize=(16, 7))
df['descricao'].value_counts().sort_values(ascending=False).head(20).plot.bar(width=0.5, edgecolor='k', align='center', linewidth=1)
plt.xlabel('Product Item', fontsize=20)
plt.ylabel('Number of transactions', fontsize=17)
ax.tick_params(labelsize=20)
plt.title('20 Most Sold Items', fontsize=20)
plt.grid()
plt.ioff() | code |
18104028/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/market_data_02.csv')
df.columns
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
hot_encoded_df = df.groupby(['nota_fiscal_id', 'descricao'])['descricao'].count().unstack().reset_index().fillna(0).set_index('nota_fiscal_id')
hot_encoded_df.head() | code |
18104028/cell_2 | [
"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/market_data_02.csv')
df.head()
df.info()
df.columns | code |
18104028/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
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 seaborn as sns
df = pd.read_csv('../input/market_data_02.csv')
df.columns
# Print the most sold items by transaction
fig, ax=plt.subplots(figsize=(16,7))
df['descricao'].value_counts().sort_values(ascending=False).head(20).plot.bar(width=0.5,edgecolor='k',align='center',linewidth=1)
plt.xlabel('Product Item',fontsize=20)
plt.ylabel('Number of transactions',fontsize=17)
ax.tick_params(labelsize=20)
plt.title('20 Most Sold Items',fontsize=20)
plt.grid()
plt.ioff()
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
hot_encoded_df = df.groupby(['nota_fiscal_id', 'descricao'])['descricao'].count().unstack().reset_index().fillna(0).set_index('nota_fiscal_id')
def encode_units(x):
if x <= 0:
return 0
if x >= 1:
return 1
hot_encoded_df = hot_encoded_df.applymap(encode_units)
frequent_itemsets = apriori(hot_encoded_df, min_support=0.01, use_colnames=True)
rules = association_rules(frequent_itemsets, metric='lift', min_threshold=1)
rules.to_csv('market_data_out_all_results.csv')
support = rules.as_matrix(columns=['support'])
confidence = rules.as_matrix(columns=['confidence'])
import seaborn as sns
for i in range(len(support)):
support[i] = support[i]
confidence[i] = confidence[i]
plt.title('Assonciation Rules')
plt.xlabel('support')
plt.ylabel('confidance')
sns.regplot(x=support, y=confidence, fit_reg=False) | code |
18104028/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import warnings
import seaborn as sns
import datetime
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
18104028/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/market_data_02.csv')
df.columns
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
hot_encoded_df = df.groupby(['nota_fiscal_id', 'descricao'])['descricao'].count().unstack().reset_index().fillna(0).set_index('nota_fiscal_id')
def encode_units(x):
if x <= 0:
return 0
if x >= 1:
return 1
hot_encoded_df = hot_encoded_df.applymap(encode_units)
frequent_itemsets = apriori(hot_encoded_df, min_support=0.01, use_colnames=True)
rules = association_rules(frequent_itemsets, metric='lift', min_threshold=1)
rules.head(10) | code |
18104028/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/market_data_02.csv')
df.columns
print('Unique products: ' + str(len(df['cod_prod'].unique()))) | code |
1004531/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
nRecords = 200000
snRecords = 1000
maindf = pd.read_csv('../input/database.csv')
maindf.drop(['Year', 'Month', 'Incident', 'City', 'Agency Name', 'Agency Type', 'Record Source', 'Agency Name'], axis=1, inplace=True)
df = maindf[maindf['Record ID'] < nRecords]
sdf = df[(maindf['Record ID'] < snRecords) & df['Victim Count'] > 0]
from IPython.display import display
df['Crime Solved'].replace('No', 0, inplace=True)
df['Crime Solved'].replace('Yes', 1, inplace=True)
print(pd.value_counts(df['Perpetrator Count']))
df.loc[df['Perpetrator Count'] <= 1, 'Perpetrator Count'] = 0
df.loc[df['Perpetrator Count'] > 1, 'Perpetrator Count'] = 1
multiple_caught = pd.value_counts(df['Perpetrator Count'] * df['Crime Solved'], sort=False)[1]
one_escape = pd.value_counts(df['Perpetrator Count'] | df['Crime Solved'], sort=False)[0]
one_caught = pd.value_counts(df['Perpetrator Count'] < df['Crime Solved'], sort=False)[True]
multiple_escape = pd.value_counts(df['Perpetrator Count'] > df['Crime Solved'], sort=False)[True]
res = np.matrix([[one_caught, multiple_caught], [one_escape, multiple_escape]]) | code |
1004531/cell_4 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
nRecords = 200000
snRecords = 1000
maindf = pd.read_csv('../input/database.csv')
maindf.drop(['Year', 'Month', 'Incident', 'City', 'Agency Name', 'Agency Type', 'Record Source', 'Agency Name'], axis=1, inplace=True)
df = maindf[maindf['Record ID'] < nRecords]
sdf = df[(maindf['Record ID'] < snRecords) & df['Victim Count'] > 0] | code |
1004531/cell_6 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
nRecords = 200000
snRecords = 1000
maindf = pd.read_csv('../input/database.csv')
maindf.drop(['Year', 'Month', 'Incident', 'City', 'Agency Name', 'Agency Type', 'Record Source', 'Agency Name'], axis=1, inplace=True)
df = maindf[maindf['Record ID'] < nRecords]
sdf = df[(maindf['Record ID'] < snRecords) & df['Victim Count'] > 0]
races = df['Perpetrator Race'].unique()
sns.jointplot(x='Perpetrator Count', y='Victim Count', data=df)
plt.show() | code |
1004531/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing
from sklearn.cross_validation import cross_val_score
from sklearn import tree
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans | code |
1004531/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
nRecords = 200000
snRecords = 1000
maindf = pd.read_csv('../input/database.csv')
maindf.drop(['Year', 'Month', 'Incident', 'City', 'Agency Name', 'Agency Type', 'Record Source', 'Agency Name'], axis=1, inplace=True)
df = maindf[maindf['Record ID'] < nRecords]
sdf = df[(maindf['Record ID'] < snRecords) & df['Victim Count'] > 0]
races = df['Perpetrator Race'].unique()
sns.countplot(x=df['Perpetrator Race'], hue=df['Crime Solved'], palette=sns.color_palette('Paired', len(races)), data=df)
plt.show()
sns.countplot(x=df['Victim Race'], hue=df['Crime Solved'], palette=sns.color_palette('Paired', len(races)), data=df)
plt.show()
sns.countplot(x=df['Victim Age'], hue=df['Crime Solved'], palette=sns.color_palette('Paired', len(races)), data=df)
plt.show() | code |
1004531/cell_3 | [
"image_output_1.png"
] | import pandas as pd
nRecords = 200000
snRecords = 1000
maindf = pd.read_csv('../input/database.csv') | code |
1004531/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
nRecords = 200000
snRecords = 1000
maindf = pd.read_csv('../input/database.csv')
maindf.drop(['Year', 'Month', 'Incident', 'City', 'Agency Name', 'Agency Type', 'Record Source', 'Agency Name'], axis=1, inplace=True)
df = maindf[maindf['Record ID'] < nRecords]
sdf = df[(maindf['Record ID'] < snRecords) & df['Victim Count'] > 0]
races = df['Perpetrator Race'].unique()
sns.swarmplot(x=sdf['Weapon'], y=sdf['Victim Count'].astype(float), data=sdf)
plt.show()
sns.countplot(x=df['Victim Sex'], hue=df['Crime Solved'], palette=sns.color_palette('Paired', len(races)), data=df)
plt.show()
sns.countplot(x=df['Weapon'], hue=df['Crime Solved'], palette=sns.color_palette('Paired', len(races)), data=df)
plt.show() | code |
50234570/cell_20 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
suicide_rates = pd.read_csv('/kaggle/input/suicide-rates-overview-1985-to-2016/master.csv')
suicide_rates.columns
def detect_outliers(df, columnNames):
outlier_indices = []
for c in columnNames:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 1))
return multiple_outliers
suicide_rates.loc[detect_outliers(suicide_rates, ['population', 'gdp_per_capita ($)', 'suicides/100k pop'])]
suicide_rates = suicide_rates.drop(detect_outliers(suicide_rates, ['population', 'gdp_per_capita ($)', 'suicides/100k pop']), axis=0).reset_index(drop=True)
year_list = list(suicide_rates.year.unique())
year_suicide_ratio = []
for i in year_list:
a = suicide_rates[suicide_rates['year'] == i]
year_suicide_rate = sum(a.index) / len(a)
year_suicide_ratio.append(year_suicide_rate)
data = pd.DataFrame({'year_list': year_list, 'year_suicide_ratio': year_suicide_ratio})
new_index = data['year_suicide_ratio'].sort_values(ascending=True).index.values
sorted_data = data.reindex(new_index)
plt.figure(figsize=(15, 10))
sns.barplot(x=sorted_data['year_list'], y=sorted_data['year_suicide_ratio'])
plt.xticks(rotation=90)
plt.xlabel('Years')
plt.ylabel('Suicides')
plt.title('Which year did the most suicides occour') | code |
50234570/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide_rates = pd.read_csv('/kaggle/input/suicide-rates-overview-1985-to-2016/master.csv')
suicide_rates.columns | code |
50234570/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50234570/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide_rates = pd.read_csv('/kaggle/input/suicide-rates-overview-1985-to-2016/master.csv')
suicide_rates.columns
suicide_rates.info() | code |
50234570/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)
suicide_rates = pd.read_csv('/kaggle/input/suicide-rates-overview-1985-to-2016/master.csv')
suicide_rates.columns
suicide_rates.describe() | code |
50234570/cell_18 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide_rates = pd.read_csv('/kaggle/input/suicide-rates-overview-1985-to-2016/master.csv')
suicide_rates.columns
def detect_outliers(df, columnNames):
outlier_indices = []
for c in columnNames:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 1))
return multiple_outliers
suicide_rates.loc[detect_outliers(suicide_rates, ['population', 'gdp_per_capita ($)', 'suicides/100k pop'])]
suicide_rates = suicide_rates.drop(detect_outliers(suicide_rates, ['population', 'gdp_per_capita ($)', 'suicides/100k pop']), axis=0).reset_index(drop=True)
suicide_rates.head() | code |
50234570/cell_15 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide_rates = pd.read_csv('/kaggle/input/suicide-rates-overview-1985-to-2016/master.csv')
suicide_rates.columns
def detect_outliers(df, columnNames):
outlier_indices = []
for c in columnNames:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 1))
return multiple_outliers
suicide_rates.loc[detect_outliers(suicide_rates, ['population', 'gdp_per_capita ($)', 'suicides/100k pop'])] | code |
50234570/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide_rates = pd.read_csv('/kaggle/input/suicide-rates-overview-1985-to-2016/master.csv')
suicide_rates.head() | code |
17121464/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
correlations = df1.corr()
names = ['hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize']
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(correlations, vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = np.arange(0, 15, 1)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(names)
ax.set_yticklabels(names)
plt.show() | code |
17121464/cell_4 | [
"text_plain_output_1.png"
] | type(df) | code |
17121464/cell_6 | [
"image_output_1.png"
] | print('Row: ', df1.shape[0])
print('Column: ', df1.shape[1]) | code |
17121464/cell_2 | [
"text_plain_output_1.png"
] | df.head() | code |
17121464/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
df.plot(kind='density', subplots=False, layout=(3, 3), sharex=False)
plt.show() | code |
17121464/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
df.hist()
plt.show() | code |
17121464/cell_3 | [
"text_plain_output_1.png"
] | df1.head() | code |
17121464/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np # linear algebra
correlations = df1.corr()
names = ['hair','feathers','eggs','milk','airborne','aquatic','predator','toothed','backbone','breathes','venomous','fins','legs','tail','domestic','catsize']
# plot correlation matrix
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(correlations, vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = np.arange(0,15,1)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(names)
ax.set_yticklabels(names)
plt.show()
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5, p=2)
knn.fit(df1[['predator', 'toothed']], df1.backbone) | code |
17121464/cell_12 | [
"text_plain_output_1.png"
] | from matplotlib.colors import ListedColormap
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np # linear algebra
correlations = df1.corr()
names = ['hair','feathers','eggs','milk','airborne','aquatic','predator','toothed','backbone','breathes','venomous','fins','legs','tail','domestic','catsize']
# plot correlation matrix
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(correlations, vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = np.arange(0,15,1)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(names)
ax.set_yticklabels(names)
plt.show()
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5, p=2)
knn.fit(df1[['predator', 'toothed']], df1.backbone)
def plotMesh():
h = 100
cmap_light = ListedColormap(['#ffffb3', '#ff9999', '#d6d6f5', '#ccffdd'])
colormap = np.array(['black', 'yellow', 'red', 'blue', 'green'])
x_min, x_max = (df1.predator.min() - 1000, df1.toothed.max() + 1000)
y_min, y_max = (df1.predator.min() - 1000, df1.toothed.max() + 1000)
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
fig = plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
plt.scatter(df1.predator, df1.toothed, c=colormap[df1.backbone], s=120)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title('4-Class classification \n(k = %i)\n Loan 1 - Yellow, Loan 2 - Red, Loan 3 - Blue, Loan 4 - green' % 5)
ax = fig.add_subplot(111)
plotMesh() | code |
17121464/cell_5 | [
"image_output_1.png"
] | print('Row: ', df.shape[0])
print('Column: ', df.shape[1]) | code |
106214047/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import math
from datetime import datetime
import requests
import seaborn as sns
sns.set()
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 10
plt.rcParams['xtick.labelsize'] = 10
plt.rcParams['ytick.labelsize'] = 10
import re
from scipy.stats import stats
from sklearn.feature_selection import SelectKBest
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV, StratifiedKFold
import xgboost
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.linear_model import LassoCV
from sklearn.linear_model import RidgeClassifierCV
from sklearn.svm import SVC
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.impute import KNNImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn import preprocessing
from imblearn.over_sampling import SMOTE
from imblearn.combine import SMOTETomek
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.linear_model import LinearRegression
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score, auc, confusion_matrix, f1_score, precision_score, recall_score, roc_curve, classification_report
from sklearn.metrics import ConfusionMatrixDisplay
import mysql.connector
from datetime import timedelta, datetime, date | code |
128047933/cell_6 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | def estimateF(img_1, img_2):
img1 = cv2.cvtColor(img_1, cv2.COLOR_BGR2GRAY)
img2 = cv2.cvtColor(img_2, cv2.COLOR_BGR2GRAY)
sift = cv2.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
cv_matches = bf.match(des1, des2)
cur_kp_1 = ArrayFromCvKps(kp1)
cur_kp_2 = ArrayFromCvKps(kp2)
matches = np.array([[m.queryIdx, m.trainIdx] for m in cv_matches])
im_matches = DrawMatches(img_1, img_2, cur_kp_1, cur_kp_2, matches)
fig = plt.figure(figsize=(25, 25))
plt.title('Matches before RANSAC')
plt.imshow(im_matches)
plt.axis('off')
plt.show()
F, inlier_mask = cv2.findFundamentalMat(cur_kp_1[matches[:, 0]], cur_kp_2[matches[:, 1]], cv2.USAC_MAGSAC, ransacReprojThreshold=0.25, confidence=0.99999, maxIters=10000)
matches_after_ransac = np.array([match for match, is_inlier in zip(matches, inlier_mask) if is_inlier])
im_inliers = DrawMatches(img_1, img_2, cur_kp_1, cur_kp_2, matches_after_ransac)
fig = plt.figure(figsize=(25, 25))
plt.title('Matches after RANSAC')
plt.imshow(im_inliers)
plt.axis('off')
plt.show()
scaling_dict = pd.read_csv(train_csv)
inlier_kp_1 = ArrayFromCvKps([kp for i, kp in enumerate(kp1) if i in matches_after_ransac[:, 0]])
inlier_kp_2 = ArrayFromCvKps([kp for i, kp in enumerate(kp2) if i in matches_after_ransac[:, 1]])
E, R, T = ComputeEssentialMatrix(F, df['K'][0], df['K'][1], inlier_kp_1, inlier_kp_2)
q = QuaternionFromMatrix(R)
T = T.flatten()
R1_gt, T1_gt = (df['R'][0], df['T'][0].reshape((3, 1)))
R2_gt, T2_gt = (df['R'][1], df['T'][1].reshape((3, 1)))
dR_gt = np.dot(R2_gt, R1_gt.T)
dT_gt = (T2_gt - np.dot(dR_gt, T1_gt)).flatten()
q_gt = QuaternionFromMatrix(dR_gt)
q_gt = q_gt / (np.linalg.norm(q_gt) + eps)
err_q, err_t = ComputeErrorForOneExample(q_gt, dT_gt, q, T, scaling_dict['scaling_factor'][0])
print(f'rotation_error={err_q:.02f} (deg), translation_error={err_t:.02f} (m)', flush=True)
estimateF(df['imgs'][0], df['imgs'][1]) | code |
128047933/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy
import math
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt
import os
eps = 1e-15
train_calibration_csvs = []
train_pair_covisibility_csvs = []
test_calibration_csvs = []
test_pair_covisibility_csvs = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if 'train' in dirname and 'pair_covisibility.csv' in filename:
train_pair_covisibility_csvs.append(os.path.join(dirname, filename))
if 'train' in dirname and 'calibration.csv' in filename:
train_calibration_csvs.append(os.path.join(dirname, filename))
if 'test' in dirname and 'pair_covisibility.csv' in filename:
test_pair_covisibility_csvs.append(os.path.join(dirname, filename))
if 'test' in dirname and 'calibration.csv' in filename:
test_calibration_csvs.append(os.path.join(dirname, filename))
test_csv = '/kaggle/input/image-matching-challenge-2022/test.csv'
train_csv = '/kaggle/input/image-matching-challenge-2022/train/scaling_factors.csv'
def getImagesFromCSV(train_csvf, calibration_csvs, pair_covisibility_csvs, category_index, pair_index):
train_csv = pd.read_csv(train_csvf)
category_csv = pd.read_csv(calibration_csvs[category_index])
pair_covisibility = pd.read_csv(pair_covisibility_csvs[category_index])
pair_covisibility = pair_covisibility[pair_covisibility['covisibility'] >= 0.1]
imgs = pair_covisibility['pair'][pair_index].split('-')
F = pair_covisibility['fundamental_matrix'][pair_index]
p_new = []
R = []
T = []
K = []
for img in imgs:
image_loc = '/kaggle/input/image-matching-challenge-2022/train/' + str(train_csv['scene'][category_index]) + '/images/' + str(img) + '.jpg'
p_new.append(cv2.imread(image_loc))
R.append(np.array(np.array(category_csv[category_csv['image_id'] == img]['rotation_matrix'])[0].split(' '), dtype=np.float32).reshape((3, 3)))
T.append(np.array(np.array(category_csv[category_csv['image_id'] == img]['translation_vector'])[0].split(' '), dtype=np.float32).reshape((3,)))
K.append(np.array(np.array(category_csv[category_csv['image_id'] == img]['camera_intrinsics'])[0].split(' '), dtype=np.float32).reshape((3, 3)))
df = pd.DataFrame({'imgs': p_new, 'R': R, 'T': T, 'K': K, 'F': F})
return df
df = getImagesFromCSV(train_csv, train_calibration_csvs, train_pair_covisibility_csvs, 0, 0)
def extract_sift(img, step_size=1):
"""
Extract SIFT features for a given grayscale image. Instead of detecting
keypoints, we will set the keypoints to be uniformly distanced pixels.
Feel free to use OpenCV functions.
Note: Check sift.compute and cv2.KeyPoint
Args:
img: Grayscale image of shape (H, W)
step_size: Size of the step between keypoints.
Return:
descriptors: numpy array of shape (int(img.shape[0]/step_size) * int(img.shape[1]/step_size), 128)
contains sift feature.
"""
sift = cv2.SIFT_create()
descriptors = np.zeros((int(img.shape[0] / step_size) * int(img.shape[1] / step_size), 128))
keypoints = [cv2.KeyPoint(x, y, step_size) for y in range(0, img.shape[0], step_size) for x in range(0, img.shape[1], step_size)]
_, descriptors = sift.compute(img, keypoints)
return descriptors
def extract_sift_for_dataset(data, step_size=1):
all_features = []
for i in range(len(data)):
img = data[i]
img = cv2.cvtColor(np.uint8(img), cv2.COLOR_BGR2GRAY)
descriptors = extract_sift(img, step_size)
all_features.append(descriptors)
# Distribution of keypoint responses
def distributionKeypointResponses(keypoints):
responses = []
for keypoint in keypoints:
responses.append(keypoint.response)
n, bins, patches = plt.hist(responses, 100,
density = 1,
color ='green',
alpha = 0.7)
plt.xlabel('Keypoint Responses')
plt.ylabel('Count')
plt.title('Distribution of Keypoint Response Intensity',
fontweight ="bold")
plt.show()
def decodeFundamental(f_matrix):
F = np.zeros((3,3))
for index, value in enumerate(f_matrix.split(" ")):
F[int(np.floor(index/3))][index%3] = float(value)
return F
def encodeFundamental(f_matrix):
F = np.zeros((9,))
for index, value in enumerate(f_matrix.ravel()):
F[index] = value
return F
def NormalizeKeypoints(keypoints, K):
C_x = K[0, 2]
C_y = K[1, 2]
f_x = K[0, 0]
f_y = K[1, 1]
keypoints = (keypoints - np.array([[C_x, C_y]])) / np.array([[f_x, f_y]])
return keypoints
def ComputeEssentialMatrix(F, K1, K2, kp1, kp2):
'''Compute the Essential matrix from the Fundamental matrix, given the calibration matrices. Note that we ask participants to estimate F, i.e., without relying on known intrinsics.'''
# Warning! Old versions of OpenCV's RANSAC could return multiple F matrices, encoded as a single matrix size 6x3 or 9x3, rather than 3x3.
# We do not account for this here, as the modern RANSACs do not do this:
# https://opencv.org/evaluating-opencvs-new-ransacs
assert F.shape[0] == 3, 'Malformed F?'
# Use OpenCV's recoverPose to solve the cheirality check:
# https://docs.opencv.org/4.5.4/d9/d0c/group__calib3d.html#gadb7d2dfcc184c1d2f496d8639f4371c0
E = np.matmul(np.matmul(K2.T, F), K1).astype(np.float64)
kp1n = NormalizeKeypoints(kp1, K1)
kp2n = NormalizeKeypoints(kp2, K2)
num_inliers, R, T, mask = cv2.recoverPose(E, kp1n, kp2n)
return E, R, T
def QuaternionFromMatrix(matrix):
'''Transform a rotation matrix into a quaternion.'''
M = np.array(matrix, dtype=np.float64, copy=False)[:4, :4]
m00 = M[0, 0]
m01 = M[0, 1]
m02 = M[0, 2]
m10 = M[1, 0]
m11 = M[1, 1]
m12 = M[1, 2]
m20 = M[2, 0]
m21 = M[2, 1]
m22 = M[2, 2]
K = np.array([[m00 - m11 - m22, 0.0, 0.0, 0.0],
[m01 + m10, m11 - m00 - m22, 0.0, 0.0],
[m02 + m20, m12 + m21, m22 - m00 - m11, 0.0],
[m21 - m12, m02 - m20, m10 - m01, m00 + m11 + m22]])
K /= 3.0
# The quaternion is the eigenvector of K that corresponds to the largest eigenvalue.
w, V = np.linalg.eigh(K)
q = V[[3, 0, 1, 2], np.argmax(w)]
if q[0] < 0:
np.negative(q, q)
return q
def ComputeErrorForOneExample(q_gt, T_gt, q, T, scale):
'''Compute the error metric for a single example.
The function returns two errors, over rotation and translation.
These are combined at different thresholds by ComputeMaa in order to compute the mean Average Accuracy.'''
q_gt_norm = q_gt / (np.linalg.norm(q_gt) + eps)
q_norm = q / (np.linalg.norm(q) + eps)
loss_q = np.maximum(eps, (1.0 - np.sum(q_norm * q_gt_norm)**2))
err_q = np.arccos(1 - 2 * loss_q)
# Apply the scaling factor for this scene.
T_gt_scaled = T_gt * scale
T_scaled = T * np.linalg.norm(T_gt) * scale / (np.linalg.norm(T) + eps)
err_t = min(np.linalg.norm(T_gt_scaled - T_scaled), np.linalg.norm(T_gt_scaled + T_scaled))
return err_q * 180 / np.pi, err_t
def BuildCompositeImage(im1, im2, axis=1, margin=0, background=1):
'''Convenience function to stack two images with different sizes.'''
if background != 0 and background != 1:
background = 1
if axis != 0 and axis != 1:
raise RuntimeError('Axis must be 0 (vertical) or 1 (horizontal')
h1, w1, _ = im1.shape
h2, w2, _ = im2.shape
if axis == 1:
composite = np.zeros((max(h1, h2), w1 + w2 + margin, 3), dtype=np.uint8) + 255 * background
if h1 > h2:
voff1, voff2 = 0, (h1 - h2) // 2
else:
voff1, voff2 = (h2 - h1) // 2, 0
hoff1, hoff2 = 0, w1 + margin
else:
composite = np.zeros((h1 + h2 + margin, max(w1, w2), 3), dtype=np.uint8) + 255 * background
if w1 > w2:
hoff1, hoff2 = 0, (w1 - w2) // 2
else:
hoff1, hoff2 = (w2 - w1) // 2, 0
voff1, voff2 = 0, h1 + margin
composite[voff1:voff1 + h1, hoff1:hoff1 + w1, :] = im1
composite[voff2:voff2 + h2, hoff2:hoff2 + w2, :] = im2
return (composite, (voff1, voff2), (hoff1, hoff2))
def DrawMatches(im1, im2, kp1, kp2, matches, axis=1, margin=0, background=0, linewidth=2):
'''Draw keypoints and matches.'''
composite, v_offset, h_offset = BuildCompositeImage(im1, im2, axis, margin, background)
# Draw all keypoints.
for coord_a, coord_b in zip(kp1, kp2):
composite = cv2.drawMarker(composite, (int(coord_a[0] + h_offset[0]), int(coord_a[1] + v_offset[0])), color=(255, 0, 0), markerType=cv2.MARKER_CROSS, markerSize=5, thickness=1)
composite = cv2.drawMarker(composite, (int(coord_b[0] + h_offset[1]), int(coord_b[1] + v_offset[1])), color=(255, 0, 0), markerType=cv2.MARKER_CROSS, markerSize=5, thickness=1)
# Draw matches, and highlight keypoints used in matches.
for idx_a, idx_b in matches:
composite = cv2.drawMarker(composite, (int(kp1[idx_a, 0] + h_offset[0]), int(kp1[idx_a, 1] + v_offset[0])), color=(0, 0, 255), markerType=cv2.MARKER_CROSS, markerSize=12, thickness=1)
composite = cv2.drawMarker(composite, (int(kp2[idx_b, 0] + h_offset[1]), int(kp2[idx_b, 1] + v_offset[1])), color=(0, 0, 255), markerType=cv2.MARKER_CROSS, markerSize=12, thickness=1)
composite = cv2.line(composite,
tuple([int(kp1[idx_a][0] + h_offset[0]),
int(kp1[idx_a][1] + v_offset[0])]),
tuple([int(kp2[idx_b][0] + h_offset[1]),
int(kp2[idx_b][1] + v_offset[1])]), color=(0, 0, 255), thickness=1)
return composite
def ArrayFromCvKps(kps):
'''Convenience function to convert OpenCV keypoints into a simple numpy array.'''
return np.array([kp.pt for kp in kps])
k = np.min(decodeFundamental(df['F'][0]))
print(encodeFundamental(F * k).reshape(9))
print(decodeFundamental(df['F'][0]).reshape((9,)))
print('Absolute Diff')
print(np.sum(np.absolute(k * encodeFundamental(F).reshape((3, 3)) - decodeFundamental(df['F'][0]))))
plt.clf()
plt.bar(np.linspace(1, 9, 9), encodeFundamental(F * k) - decodeFundamental(df['F'][0]).reshape((9,)))
plt.show() | code |
128047933/cell_3 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy
import math
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt
import os
eps = 1e-15
train_calibration_csvs = []
train_pair_covisibility_csvs = []
test_calibration_csvs = []
test_pair_covisibility_csvs = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if 'train' in dirname and 'pair_covisibility.csv' in filename:
train_pair_covisibility_csvs.append(os.path.join(dirname, filename))
if 'train' in dirname and 'calibration.csv' in filename:
train_calibration_csvs.append(os.path.join(dirname, filename))
if 'test' in dirname and 'pair_covisibility.csv' in filename:
test_pair_covisibility_csvs.append(os.path.join(dirname, filename))
if 'test' in dirname and 'calibration.csv' in filename:
test_calibration_csvs.append(os.path.join(dirname, filename))
test_csv = '/kaggle/input/image-matching-challenge-2022/test.csv'
train_csv = '/kaggle/input/image-matching-challenge-2022/train/scaling_factors.csv'
def getImagesFromCSV(train_csvf, calibration_csvs, pair_covisibility_csvs, category_index, pair_index):
train_csv = pd.read_csv(train_csvf)
category_csv = pd.read_csv(calibration_csvs[category_index])
pair_covisibility = pd.read_csv(pair_covisibility_csvs[category_index])
pair_covisibility = pair_covisibility[pair_covisibility['covisibility'] >= 0.1]
imgs = pair_covisibility['pair'][pair_index].split('-')
F = pair_covisibility['fundamental_matrix'][pair_index]
p_new = []
R = []
T = []
K = []
for img in imgs:
image_loc = '/kaggle/input/image-matching-challenge-2022/train/' + str(train_csv['scene'][category_index]) + '/images/' + str(img) + '.jpg'
p_new.append(cv2.imread(image_loc))
R.append(np.array(np.array(category_csv[category_csv['image_id'] == img]['rotation_matrix'])[0].split(' '), dtype=np.float32).reshape((3, 3)))
T.append(np.array(np.array(category_csv[category_csv['image_id'] == img]['translation_vector'])[0].split(' '), dtype=np.float32).reshape((3,)))
K.append(np.array(np.array(category_csv[category_csv['image_id'] == img]['camera_intrinsics'])[0].split(' '), dtype=np.float32).reshape((3, 3)))
df = pd.DataFrame({'imgs': p_new, 'R': R, 'T': T, 'K': K, 'F': F})
return df
df = getImagesFromCSV(train_csv, train_calibration_csvs, train_pair_covisibility_csvs, 0, 0)
print(df['imgs'][0].shape) | code |
90140147/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import data_utils
import pandas as pd
import pandas as pd
import data_utils
holiday_df = pd.read_csv('../input/singapore-holiday/holiday.csv')
df = data_utils.sg_holiday_feature(holiday_df=holiday_df.copy(), startDate='20140101', endDate='20211231', holiday_dummy=False)
df, dist = data_utils.set_label(df=df, label_column='Holiday')
df = data_utils.get_date_dummy(df, date_column='DATE')
df.set_index('DATE', inplace=True)
df = df.drop(columns=['Day'])
df = df['2014-01-01':'2021-12-31']
train_data = data_utils.switch_y_column(df=df.copy(), column_name='Holiday')
X_train_seq, y_train_seq, X_val_seq, y_val_seq = data_utils.split_sequence(train_data.values, look_back=look_back, look_forward=look_forward, split_val=True, print_shape=True)
n_features = X_train_seq.shape[2] | code |
90140147/cell_2 | [
"text_html_output_1.png"
] | import data_utils
import pandas as pd
import pandas as pd
import data_utils
holiday_df = pd.read_csv('../input/singapore-holiday/holiday.csv')
df = data_utils.sg_holiday_feature(holiday_df=holiday_df.copy(), startDate='20140101', endDate='20211231', holiday_dummy=False)
df, dist = data_utils.set_label(df=df, label_column='Holiday')
df = data_utils.get_date_dummy(df, date_column='DATE')
df.set_index('DATE', inplace=True)
df = df.drop(columns=['Day'])
df = df['2014-01-01':'2021-12-31']
df.tail() | code |
90139583/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum()
def outlier_check(data_check):
q1 = data_check.quantile(0.25)
q3 = data_check.quantile(0.75)
iqr = q3 - q1
lower_limit = q1 - 1.5 * iqr
upper_limi = q3 + 1.5 * iqr
lower_outlier = data_check < lower_limit
upper_outlier = data_check > upper_limi
return data_check[lower_outlier | upper_outlier]
for col_name, values in housing_data.items():
if housing_data[col_name].dtype == 'float64':
percntge = len(outlier_check(housing_data[col_name])) / len(housing_data) * 100
plt.figure(figsize=(18, 18))
sns.heatmap(housing_data.corr(), annot=True, cmap='RdYlGn')
plt.show() | code |
90139583/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.describe() | code |
90139583/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)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape | code |
90139583/cell_26 | [
"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
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum()
def outlier_check(data_check):
q1 = data_check.quantile(0.25)
q3 = data_check.quantile(0.75)
iqr = q3 - q1
lower_limit = q1 - 1.5 * iqr
upper_limi = q3 + 1.5 * iqr
lower_outlier = data_check < lower_limit
upper_outlier = data_check > upper_limi
return data_check[lower_outlier | upper_outlier]
for col_name, values in housing_data.items():
if housing_data[col_name].dtype == 'float64':
percntge = len(outlier_check(housing_data[col_name])) / len(housing_data) * 100
corr_matrix = housing_data.corr()
corr_matrix
corr_matrix['median_house_value'].sort_values(ascending=False)
housing_data.plot(kind='scatter', x='longitude', y='latitude', alpha=0.4, s=housing_data['population'] / 100, label='population', figsize=(10, 7), c='median_house_value', cmap=plt.get_cmap('jet'), colorbar=True, title='Visualizing Geographical Data') | code |
90139583/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)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any() | code |
90139583/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 |
90139583/cell_7 | [
"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)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.head() | code |
90139583/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum()
def outlier_check(data_check):
q1 = data_check.quantile(0.25)
q3 = data_check.quantile(0.75)
iqr = q3 - q1
lower_limit = q1 - 1.5 * iqr
upper_limi = q3 + 1.5 * iqr
lower_outlier = data_check < lower_limit
upper_outlier = data_check > upper_limi
return data_check[lower_outlier | upper_outlier]
for col_name, values in housing_data.items():
if housing_data[col_name].dtype == 'float64':
percntge = len(outlier_check(housing_data[col_name])) / len(housing_data) * 100
housing_data.hist(bins=70, figsize=(20, 15))
plt.show() | code |
90139583/cell_28 | [
"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
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum()
def outlier_check(data_check):
q1 = data_check.quantile(0.25)
q3 = data_check.quantile(0.75)
iqr = q3 - q1
lower_limit = q1 - 1.5 * iqr
upper_limi = q3 + 1.5 * iqr
lower_outlier = data_check < lower_limit
upper_outlier = data_check > upper_limi
return data_check[lower_outlier | upper_outlier]
for col_name, values in housing_data.items():
if housing_data[col_name].dtype == 'float64':
percntge = len(outlier_check(housing_data[col_name])) / len(housing_data) * 100
corr_matrix = housing_data.corr()
corr_matrix
corr_matrix['median_house_value'].sort_values(ascending=False)
plt.figure(figsize=(10, 7))
plt.title('median_house_value vs ocean_proximity')
sns.stripplot(data=housing_data, x='ocean_proximity', y='median_house_value', jitter=0.2) | code |
90139583/cell_15 | [
"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)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum()
housing_data.plot(kind='box', figsize=(20, 15), subplots=True, layout=(3, 3))
plt.show() | code |
90139583/cell_16 | [
"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)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum()
def outlier_check(data_check):
q1 = data_check.quantile(0.25)
q3 = data_check.quantile(0.75)
iqr = q3 - q1
lower_limit = q1 - 1.5 * iqr
upper_limi = q3 + 1.5 * iqr
lower_outlier = data_check < lower_limit
upper_outlier = data_check > upper_limi
return data_check[lower_outlier | upper_outlier]
for col_name, values in housing_data.items():
if housing_data[col_name].dtype == 'float64':
percntge = len(outlier_check(housing_data[col_name])) / len(housing_data) * 100
print(col_name, ':', percntge, '%') | code |
90139583/cell_24 | [
"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
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum()
def outlier_check(data_check):
q1 = data_check.quantile(0.25)
q3 = data_check.quantile(0.75)
iqr = q3 - q1
lower_limit = q1 - 1.5 * iqr
upper_limi = q3 + 1.5 * iqr
lower_outlier = data_check < lower_limit
upper_outlier = data_check > upper_limi
return data_check[lower_outlier | upper_outlier]
for col_name, values in housing_data.items():
if housing_data[col_name].dtype == 'float64':
percntge = len(outlier_check(housing_data[col_name])) / len(housing_data) * 100
corr_matrix = housing_data.corr()
corr_matrix
corr_matrix['median_house_value'].sort_values(ascending=False) | code |
90139583/cell_22 | [
"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
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum()
def outlier_check(data_check):
q1 = data_check.quantile(0.25)
q3 = data_check.quantile(0.75)
iqr = q3 - q1
lower_limit = q1 - 1.5 * iqr
upper_limi = q3 + 1.5 * iqr
lower_outlier = data_check < lower_limit
upper_outlier = data_check > upper_limi
return data_check[lower_outlier | upper_outlier]
for col_name, values in housing_data.items():
if housing_data[col_name].dtype == 'float64':
percntge = len(outlier_check(housing_data[col_name])) / len(housing_data) * 100
sns.pairplot(housing_data, diag_kind='kde') | code |
90139583/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.info() | code |
90139583/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing_data = pd.read_csv('/kaggle/input/california-housing-prices/housing.csv')
data = housing_data.copy()
housing_data.shape
housing_data.duplicated().values.any()
housing_data.isnull().sum() | code |
32062272/cell_21 | [
"text_plain_output_1.png"
] | from time import time
import inverness
import pandas as pd
import re
import inverness
model = inverness.Model('/kaggle/input/cord-19-inverness-all-v7/').load(['fun', 'meta', 'phraser', 'dictionary', 'tfidf', 'lsi', 'dense_ann'])
pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv', nrows=3)
meta_by_sha = {}
meta_by_pmc = {}
t0 = time()
COLS = ['cord_uid', 'sha', 'pmcid', 'publish_time', 'journal', 'url', 'title', 'authors']
df = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
selected = df[df['full_text_file'] != ''][COLS]
rows = selected.iterrows()
for _, r in rows:
if type(r['sha']) is str:
for sha in r['sha'].split(';'):
sha = sha.strip()
meta = {k: r[k] for k in COLS}
meta_by_sha[sha] = meta
if type(r['pmcid']) is str:
pmc = r['pmcid']
meta = {k: r[k] for k in COLS}
meta_by_pmc[pmc] = meta
def score_text(text, criteria):
""""""
total = 0
value = 1
for c in criteria:
if type(c) in (int, float):
value = c
else:
c = c.replace('_', '\\b')
matches = re.findall(c, text, re.I)
cnt = len(matches)
total += value * cnt_to_score(cnt)
return total
def cnt_to_score(cnt):
return min(2, cnt)
def score_results(i_d_lists, criteria):
""""""
results = []
for i, d in zip(*i_d_lists):
score = 0
paper_id = model.meta[i]['paper_id']
if paper_id in meta_by_sha:
meta = meta_by_sha[paper_id]
else:
meta = meta_by_pmc[paper_id]
score += score_text(meta['title'], criteria)
doc = model.get_doc(i)
text = model.doc_to_text(doc).replace('\n', ' ').replace('\r', ' ')
html = highlight(text, criteria, style_by_group_id, default_style)
score += score_text(text, criteria)
rec = (score, d, i, html, meta)
results += [rec]
results.sort(key=lambda x: (-x[0], x[1]))
return results
def score_queries(queries, criteria, K=50):
""""""
by_score = []
for query in queries:
q = model.text_to_dense(query)
i_d = model.dense_ann_query(q, K)
results = score_results(i_d, criteria)
score = agg_results(results)
by_score += [(score, query)]
by_score.sort()
return by_score
def highlight(text, criteria, styles={}, default='w=bold'):
""""""
group_id = 0
for c in criteria:
if type(c) in (int, float):
group_id += 1
else:
c = c.replace('_', '\\b')
c = f'({c}\\w*)'
style = styles.get(group_id, default)
style_props = []
for prop in style.split(','):
k, _, v = prop.partition('=')
if k == 'w':
style_props += [f'font-weight:{v}']
if k == 'fg':
style_props += [f'color:{v}']
if k == 'bg':
style_props += [f'background-color:{v}']
before = f'''<span style="{';'.join(style_props)}">'''
after = '</span>'
text = re.sub(c, before + '\\1' + after, text, flags=re.I)
return text
def agg_results(results):
""""""
scores = [x[0] for x in results]
return sum([x * x for x in scores]) ** 0.5
def plot_results(results, title=''):
""""""
scores = [x[0] for x in results]
scores.sort(reverse=True)
score = agg_results(results)
plt.figtext(0.4, 1, f'total L2 score: {score:.02f}')
criteria = [100, 'mechanical', 'ventilat', 20, 'adjust', '_age', '_years', '_old', 'elder', 'young', 2, '_surviv', 'discharge', 'extubate', 'alive', 2, 'nonsurviv', 'non-surviv', '_died', 'dead', 'death', 'mortality', 'complication', 10, 'Kaplan.Meier', 'APACHE', 'SOFA', 'RIFLE', 'Glasgow.Coma', 'GCS', 'SAPS', '_RESP_', 'RSBI', '1000.person_', 10, 'figure \\d+', '_fig[.]\\s*\\d+', '_table \\d+', 2, 'outcome', 'result', 'occurr', 'cohort', 'median', '_n\\s*=\\s*\\d+', '(?<=[ (])\\d+ patients', '(?<=[ (])\\d+ cases', 150, 'covid|sars-cov|cov-2|cov2|wuhan']
style_by_group_id = {1: 'w=bold', 2: 'bg=#FFFF00', 3: 'bg=#00FF00', 4: 'bg=#FFAAAA', 5: 'bg=#FFCC00', 6: 'bg=#FFAAFF', 7: 'bg=#00FFFF', 9: 'w=bold,fg=#FF0000'}
default_style = ''
K = 50
queries = ['Outcomes data for COVID-19 after mechanical ventilation adjusted for age', 'Outcomes data for COVID-19 / SARS-CoV-2 after mechanical ventilation adjusted for age', 'Results for COVID-19 / SARS-CoV-2 after mechanical ventilation adjusted for age', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died survived survivors adjusted age years old', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived survivors extubated adjusted', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died survived survivors adjusted age years old', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived extubated adjusted age', 'COVID-19 SARS-CoV-2 outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted age', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated', 'COVID-19 SARS-CoV-2 results outcomes mechnical ventilation discharged died survived extubated', 'COVID-19 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'Covid-19 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 covid-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 COVID-2019 SARS-CoV-2 SARS-CoV2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcmes data after mechnical ventilation discharged died survived extubated adjusted age']
query = 'Outcomes data for COVID-19 after mechanical ventilation adjusted for age'
K = 500
q = model.text_to_dense(query)
i_d_lists = model.dense_ann_query(q, K)
results = score_results(i_d_lists, criteria)
plot_results(results, title='Query result score by rank (descencing scores)') | code |
32062272/cell_13 | [
"text_html_output_1.png"
] | from pprint import pprint
from time import time
import pandas as pd
pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv', nrows=3)
meta_by_sha = {}
meta_by_pmc = {}
t0 = time()
COLS = ['cord_uid', 'sha', 'pmcid', 'publish_time', 'journal', 'url', 'title', 'authors']
df = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
selected = df[df['full_text_file'] != ''][COLS]
rows = selected.iterrows()
for _, r in rows:
if type(r['sha']) is str:
for sha in r['sha'].split(';'):
sha = sha.strip()
meta = {k: r[k] for k in COLS}
meta_by_sha[sha] = meta
if type(r['pmcid']) is str:
pmc = r['pmcid']
meta = {k: r[k] for k in COLS}
meta_by_pmc[pmc] = meta
print('Paper metadata sample:\n')
for sha in meta_by_sha:
pprint(meta_by_sha[sha])
break | code |
32062272/cell_23 | [
"text_plain_output_1.png"
] | from IPython.core.display import display, HTML
from time import time
import inverness
import pandas as pd
import re
import inverness
model = inverness.Model('/kaggle/input/cord-19-inverness-all-v7/').load(['fun', 'meta', 'phraser', 'dictionary', 'tfidf', 'lsi', 'dense_ann'])
pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv', nrows=3)
meta_by_sha = {}
meta_by_pmc = {}
t0 = time()
COLS = ['cord_uid', 'sha', 'pmcid', 'publish_time', 'journal', 'url', 'title', 'authors']
df = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
selected = df[df['full_text_file'] != ''][COLS]
rows = selected.iterrows()
for _, r in rows:
if type(r['sha']) is str:
for sha in r['sha'].split(';'):
sha = sha.strip()
meta = {k: r[k] for k in COLS}
meta_by_sha[sha] = meta
if type(r['pmcid']) is str:
pmc = r['pmcid']
meta = {k: r[k] for k in COLS}
meta_by_pmc[pmc] = meta
def score_text(text, criteria):
""""""
total = 0
value = 1
for c in criteria:
if type(c) in (int, float):
value = c
else:
c = c.replace('_', '\\b')
matches = re.findall(c, text, re.I)
cnt = len(matches)
total += value * cnt_to_score(cnt)
return total
def cnt_to_score(cnt):
return min(2, cnt)
def score_results(i_d_lists, criteria):
""""""
results = []
for i, d in zip(*i_d_lists):
score = 0
paper_id = model.meta[i]['paper_id']
if paper_id in meta_by_sha:
meta = meta_by_sha[paper_id]
else:
meta = meta_by_pmc[paper_id]
score += score_text(meta['title'], criteria)
doc = model.get_doc(i)
text = model.doc_to_text(doc).replace('\n', ' ').replace('\r', ' ')
html = highlight(text, criteria, style_by_group_id, default_style)
score += score_text(text, criteria)
rec = (score, d, i, html, meta)
results += [rec]
results.sort(key=lambda x: (-x[0], x[1]))
return results
def score_queries(queries, criteria, K=50):
""""""
by_score = []
for query in queries:
q = model.text_to_dense(query)
i_d = model.dense_ann_query(q, K)
results = score_results(i_d, criteria)
score = agg_results(results)
by_score += [(score, query)]
by_score.sort()
return by_score
def highlight(text, criteria, styles={}, default='w=bold'):
""""""
group_id = 0
for c in criteria:
if type(c) in (int, float):
group_id += 1
else:
c = c.replace('_', '\\b')
c = f'({c}\\w*)'
style = styles.get(group_id, default)
style_props = []
for prop in style.split(','):
k, _, v = prop.partition('=')
if k == 'w':
style_props += [f'font-weight:{v}']
if k == 'fg':
style_props += [f'color:{v}']
if k == 'bg':
style_props += [f'background-color:{v}']
before = f'''<span style="{';'.join(style_props)}">'''
after = '</span>'
text = re.sub(c, before + '\\1' + after, text, flags=re.I)
return text
def agg_results(results):
""""""
scores = [x[0] for x in results]
return sum([x * x for x in scores]) ** 0.5
def plot_results(results, title=''):
""""""
scores = [x[0] for x in results]
scores.sort(reverse=True)
score = agg_results(results)
plt.figtext(0.4, 1, f'total L2 score: {score:.02f}')
criteria = [100, 'mechanical', 'ventilat', 20, 'adjust', '_age', '_years', '_old', 'elder', 'young', 2, '_surviv', 'discharge', 'extubate', 'alive', 2, 'nonsurviv', 'non-surviv', '_died', 'dead', 'death', 'mortality', 'complication', 10, 'Kaplan.Meier', 'APACHE', 'SOFA', 'RIFLE', 'Glasgow.Coma', 'GCS', 'SAPS', '_RESP_', 'RSBI', '1000.person_', 10, 'figure \\d+', '_fig[.]\\s*\\d+', '_table \\d+', 2, 'outcome', 'result', 'occurr', 'cohort', 'median', '_n\\s*=\\s*\\d+', '(?<=[ (])\\d+ patients', '(?<=[ (])\\d+ cases', 150, 'covid|sars-cov|cov-2|cov2|wuhan']
style_by_group_id = {1: 'w=bold', 2: 'bg=#FFFF00', 3: 'bg=#00FF00', 4: 'bg=#FFAAAA', 5: 'bg=#FFCC00', 6: 'bg=#FFAAFF', 7: 'bg=#00FFFF', 9: 'w=bold,fg=#FF0000'}
default_style = ''
K = 50
queries = ['Outcomes data for COVID-19 after mechanical ventilation adjusted for age', 'Outcomes data for COVID-19 / SARS-CoV-2 after mechanical ventilation adjusted for age', 'Results for COVID-19 / SARS-CoV-2 after mechanical ventilation adjusted for age', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died survived survivors adjusted age years old', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived survivors extubated adjusted', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died survived survivors adjusted age years old', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived extubated adjusted age', 'COVID-19 SARS-CoV-2 outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted age', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated', 'COVID-19 SARS-CoV-2 results outcomes mechnical ventilation discharged died survived extubated', 'COVID-19 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'Covid-19 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 covid-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 COVID-2019 SARS-CoV-2 SARS-CoV2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcmes data after mechnical ventilation discharged died survived extubated adjusted age']
query = 'Outcomes data for COVID-19 after mechanical ventilation adjusted for age'
K = 500
q = model.text_to_dense(query)
i_d_lists = model.dense_ann_query(q, K)
results = score_results(i_d_lists, criteria)
N = 20
for score, dist, i, html, meta in results[:N]:
display(HTML(f'''\n <h3>{meta['title']}</h3>\n <p>{meta['publish_time']} -- {meta['journal']} -- <a href="{meta['url']}">link</a></p>\n <p style="color:#AAAAAA">score:{score} -- dist:{dist:.03f} -- cord_uid:{meta['cord_uid']} -- paragraph_id:{i}</p>\n {html}\n ''')) | code |
32062272/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv', nrows=3) | code |
32062272/cell_19 | [
"text_plain_output_1.png"
] | from time import time
import inverness
import pandas as pd
import re
import inverness
model = inverness.Model('/kaggle/input/cord-19-inverness-all-v7/').load(['fun', 'meta', 'phraser', 'dictionary', 'tfidf', 'lsi', 'dense_ann'])
pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv', nrows=3)
meta_by_sha = {}
meta_by_pmc = {}
t0 = time()
COLS = ['cord_uid', 'sha', 'pmcid', 'publish_time', 'journal', 'url', 'title', 'authors']
df = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
selected = df[df['full_text_file'] != ''][COLS]
rows = selected.iterrows()
for _, r in rows:
if type(r['sha']) is str:
for sha in r['sha'].split(';'):
sha = sha.strip()
meta = {k: r[k] for k in COLS}
meta_by_sha[sha] = meta
if type(r['pmcid']) is str:
pmc = r['pmcid']
meta = {k: r[k] for k in COLS}
meta_by_pmc[pmc] = meta
def score_text(text, criteria):
""""""
total = 0
value = 1
for c in criteria:
if type(c) in (int, float):
value = c
else:
c = c.replace('_', '\\b')
matches = re.findall(c, text, re.I)
cnt = len(matches)
total += value * cnt_to_score(cnt)
return total
def cnt_to_score(cnt):
return min(2, cnt)
def score_results(i_d_lists, criteria):
""""""
results = []
for i, d in zip(*i_d_lists):
score = 0
paper_id = model.meta[i]['paper_id']
if paper_id in meta_by_sha:
meta = meta_by_sha[paper_id]
else:
meta = meta_by_pmc[paper_id]
score += score_text(meta['title'], criteria)
doc = model.get_doc(i)
text = model.doc_to_text(doc).replace('\n', ' ').replace('\r', ' ')
html = highlight(text, criteria, style_by_group_id, default_style)
score += score_text(text, criteria)
rec = (score, d, i, html, meta)
results += [rec]
results.sort(key=lambda x: (-x[0], x[1]))
return results
def score_queries(queries, criteria, K=50):
""""""
by_score = []
for query in queries:
q = model.text_to_dense(query)
i_d = model.dense_ann_query(q, K)
results = score_results(i_d, criteria)
score = agg_results(results)
by_score += [(score, query)]
by_score.sort()
return by_score
def highlight(text, criteria, styles={}, default='w=bold'):
""""""
group_id = 0
for c in criteria:
if type(c) in (int, float):
group_id += 1
else:
c = c.replace('_', '\\b')
c = f'({c}\\w*)'
style = styles.get(group_id, default)
style_props = []
for prop in style.split(','):
k, _, v = prop.partition('=')
if k == 'w':
style_props += [f'font-weight:{v}']
if k == 'fg':
style_props += [f'color:{v}']
if k == 'bg':
style_props += [f'background-color:{v}']
before = f'''<span style="{';'.join(style_props)}">'''
after = '</span>'
text = re.sub(c, before + '\\1' + after, text, flags=re.I)
return text
def agg_results(results):
""""""
scores = [x[0] for x in results]
return sum([x * x for x in scores]) ** 0.5
def plot_results(results, title=''):
""""""
scores = [x[0] for x in results]
scores.sort(reverse=True)
score = agg_results(results)
plt.figtext(0.4, 1, f'total L2 score: {score:.02f}')
criteria = [100, 'mechanical', 'ventilat', 20, 'adjust', '_age', '_years', '_old', 'elder', 'young', 2, '_surviv', 'discharge', 'extubate', 'alive', 2, 'nonsurviv', 'non-surviv', '_died', 'dead', 'death', 'mortality', 'complication', 10, 'Kaplan.Meier', 'APACHE', 'SOFA', 'RIFLE', 'Glasgow.Coma', 'GCS', 'SAPS', '_RESP_', 'RSBI', '1000.person_', 10, 'figure \\d+', '_fig[.]\\s*\\d+', '_table \\d+', 2, 'outcome', 'result', 'occurr', 'cohort', 'median', '_n\\s*=\\s*\\d+', '(?<=[ (])\\d+ patients', '(?<=[ (])\\d+ cases', 150, 'covid|sars-cov|cov-2|cov2|wuhan']
style_by_group_id = {1: 'w=bold', 2: 'bg=#FFFF00', 3: 'bg=#00FF00', 4: 'bg=#FFAAAA', 5: 'bg=#FFCC00', 6: 'bg=#FFAAFF', 7: 'bg=#00FFFF', 9: 'w=bold,fg=#FF0000'}
default_style = ''
K = 50
queries = ['Outcomes data for COVID-19 after mechanical ventilation adjusted for age', 'Outcomes data for COVID-19 / SARS-CoV-2 after mechanical ventilation adjusted for age', 'Results for COVID-19 / SARS-CoV-2 after mechanical ventilation adjusted for age', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died survived survivors adjusted age years old', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived survivors extubated adjusted', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged dead died survived survivors adjusted age years old', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results after mechnical ventilation discharged died survived extubated adjusted age', 'COVID-19 SARS-CoV-2 outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted age', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated', 'COVID-19 SARS-CoV-2 results outcomes mechnical ventilation discharged died survived extubated', 'COVID-19 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'Covid-19 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 covid-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 COVID-2019 SARS-CoV-2 SARS-CoV2 results outcomes after mechnical ventilation discharged died survived extubated adjusted', 'COVID-19 SARS-CoV-2 results outcmes data after mechnical ventilation discharged died survived extubated adjusted age']
for score, query in score_queries(queries, criteria, K):
print(f'{score:10.02f} -- {query}') | code |
32062272/cell_7 | [
"text_plain_output_1.png"
] | import inverness
import inverness
model = inverness.Model('/kaggle/input/cord-19-inverness-all-v7/').load(['fun', 'meta', 'phraser', 'dictionary', 'tfidf', 'lsi', 'dense_ann']) | code |
32062272/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from time import time
import pandas as pd
pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv', nrows=3)
meta_by_sha = {}
meta_by_pmc = {}
t0 = time()
COLS = ['cord_uid', 'sha', 'pmcid', 'publish_time', 'journal', 'url', 'title', 'authors']
df = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
selected = df[df['full_text_file'] != ''][COLS]
rows = selected.iterrows()
for _, r in rows:
if type(r['sha']) is str:
for sha in r['sha'].split(';'):
sha = sha.strip()
meta = {k: r[k] for k in COLS}
meta_by_sha[sha] = meta
if type(r['pmcid']) is str:
pmc = r['pmcid']
meta = {k: r[k] for k in COLS}
meta_by_pmc[pmc] = meta
print(f'done in {time() - t0:.01f} seconds') | code |
32062272/cell_5 | [
"text_html_output_10.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_15.png",
"text_html_output_5.png",
"text_html_output_14.png",
"text_html_output_19.png",
"text_html_output_9.png",
"text_html_output_13.png",
"text_html_output_20.png",
"text_html_output_1.png",
"text_html_output_17.png",
"text_html_output_18.png",
"text_html_output_12.png",
"text_html_output_11.png",
"text_html_output_8.png",
"text_html_output_3.png",
"text_html_output_7.png"
] | !pip install inverness | code |
105171993/cell_4 | [
"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)
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes | code |
105171993/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)
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes
leagues5 = inex.groupby(by=['League', 'Year'])['Expenditure', 'Arrivals', 'Income', 'Depatures', 'Balance'].sum() | code |
105171993/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 |
105171993/cell_7 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes
leagues5 = inex.groupby(by=['League', 'Year'])['Expenditure', 'Arrivals', 'Income', 'Depatures', 'Balance'].sum()
leagues11 = inex[(inex['League'] == 'LaLiga') | (inex['League'] == 'Premier League') | (inex['League'] == 'Serie A') | (inex['League'] == 'Ligue 1') | (inex['League'] == 'Bundesliga')]
leagues11 | code |
105171993/cell_15 | [
"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
import seaborn as sns
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes
leagues5 = inex.groupby(by=['League', 'Year'])['Expenditure', 'Arrivals', 'Income', 'Depatures', 'Balance'].sum()
leagues11 = inex[(inex['League'] == 'LaLiga') | (inex['League'] == 'Premier League') | (inex['League'] == 'Serie A') | (inex['League'] == 'Ligue 1') | (inex['League'] == 'Bundesliga')]
leagues11
#Number of Players Transfered per league last 12 years
f, ax1 = plt.subplots(figsize=(15, 8))
sns.set_style('whitegrid')
ax1 = sns.barplot(y = 'Arrivals' , x = 'Year',hue = 'League', data = leagues11,estimator = sum, ci = False,
palette = sns.color_palette("husl",5) )
ax1.bar_label(ax1.containers[2], color = 'black', size = 15)
#ax1.bar_label(ax1.containers[3], color = 'black', size = 9)
ax1.tick_params(labelsize = 15)
plt.xticks(rotation = 0, size = 15)
plt.yticks(size = 15)
plt.xlabel('Year', size = 18)
plt.ylabel('Number of Transfers', size = 18)
plt.title('Total Number of Players Transfered Per League [2011 to 2022]', size = 20)
#Bar Plot
import seaborn as sns
#palette = sns.color_palette(['darkblue','red','green','yellow','pink'])
sns.set(rc={'figure.figsize':(15,8)})
sns.set_style('whitegrid')
plot = sns.barplot(y = 'Expenditure' , x = 'Year',
data = leagues11, estimator = sum,
hue = 'League' ,ci = False, palette = sns.color_palette("husl", 5))
plot1 = plot.get_figure()
plt.xlabel('Year', size = 15)
plt.ylabel('Transfer Value (Million Euros)', size = 18)
plt.title('Money Spent by Europes Top 5 Leagues [2011 to 2022]', size = 20)
plot1.savefig("BarPlot.png")
sns.set(rc={'figure.figsize': (15, 8)})
sns.set_style('white')
sns.set(rc={'figure.figsize': (15, 8)})
sns.set_style('whitegrid')
sns.histplot(y='Expenditure', x='League', hue='League', data=leagues5, palette=sns.color_palette(['olive', 'teal', 'darkorchid', 'lightcoral', 'seagreen']))
plt.xlabel('Leagues', size=15)
plt.ylabel('Transfer Value(Million Euros)', size=18)
plt.xticks(rotation=0, size=15)
plt.yticks(size=15)
plt.savefig('histplotClubs3.jpg') | code |
105171993/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes
leagues5 = inex.groupby(by=['League', 'Year'])['Expenditure', 'Arrivals', 'Income', 'Depatures', 'Balance'].sum()
leagues11 = inex[(inex['League'] == 'LaLiga') | (inex['League'] == 'Premier League') | (inex['League'] == 'Serie A') | (inex['League'] == 'Ligue 1') | (inex['League'] == 'Bundesliga')]
leagues11
#Number of Players Transfered per league last 12 years
f, ax1 = plt.subplots(figsize=(15, 8))
sns.set_style('whitegrid')
ax1 = sns.barplot(y = 'Arrivals' , x = 'Year',hue = 'League', data = leagues11,estimator = sum, ci = False,
palette = sns.color_palette("husl",5) )
ax1.bar_label(ax1.containers[2], color = 'black', size = 15)
#ax1.bar_label(ax1.containers[3], color = 'black', size = 9)
ax1.tick_params(labelsize = 15)
plt.xticks(rotation = 0, size = 15)
plt.yticks(size = 15)
plt.xlabel('Year', size = 18)
plt.ylabel('Number of Transfers', size = 18)
plt.title('Total Number of Players Transfered Per League [2011 to 2022]', size = 20)
#Bar Plot
import seaborn as sns
#palette = sns.color_palette(['darkblue','red','green','yellow','pink'])
sns.set(rc={'figure.figsize':(15,8)})
sns.set_style('whitegrid')
plot = sns.barplot(y = 'Expenditure' , x = 'Year',
data = leagues11, estimator = sum,
hue = 'League' ,ci = False, palette = sns.color_palette("husl", 5))
plot1 = plot.get_figure()
plt.xlabel('Year', size = 15)
plt.ylabel('Transfer Value (Million Euros)', size = 18)
plt.title('Money Spent by Europes Top 5 Leagues [2011 to 2022]', size = 20)
plot1.savefig("BarPlot.png")
sns.set(rc={'figure.figsize': (15, 8)})
sns.set_style('white')
sns.set(rc={'figure.figsize': (15, 8)})
sns.set_style('whitegrid')
plt.xticks(rotation=0, size=15)
plt.yticks(size=15)
sns.set_style('whitegrid')
sns.set(rc={'figure.figsize': (15, 8)})
sns.relplot(y='Expenditure', x='Year', ci=None, hue='League', estimator=sum, palette=sns.color_palette('husl', 5), kind='line', data=leagues11[leagues11.League.isin(['Serie A', 'Premier League', 'LaLiga', 'Bundesliga', 'Ligue 1'])]) | code |
105171993/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes
leagues5 = inex.groupby(by=['League', 'Year'])['Expenditure', 'Arrivals', 'Income', 'Depatures', 'Balance'].sum()
leagues11 = inex[(inex['League'] == 'LaLiga') | (inex['League'] == 'Premier League') | (inex['League'] == 'Serie A') | (inex['League'] == 'Ligue 1') | (inex['League'] == 'Bundesliga')]
leagues11
#Number of Players Transfered per league last 12 years
f, ax1 = plt.subplots(figsize=(15, 8))
sns.set_style('whitegrid')
ax1 = sns.barplot(y = 'Arrivals' , x = 'Year',hue = 'League', data = leagues11,estimator = sum, ci = False,
palette = sns.color_palette("husl",5) )
ax1.bar_label(ax1.containers[2], color = 'black', size = 15)
#ax1.bar_label(ax1.containers[3], color = 'black', size = 9)
ax1.tick_params(labelsize = 15)
plt.xticks(rotation = 0, size = 15)
plt.yticks(size = 15)
plt.xlabel('Year', size = 18)
plt.ylabel('Number of Transfers', size = 18)
plt.title('Total Number of Players Transfered Per League [2011 to 2022]', size = 20)
#Bar Plot
import seaborn as sns
#palette = sns.color_palette(['darkblue','red','green','yellow','pink'])
sns.set(rc={'figure.figsize':(15,8)})
sns.set_style('whitegrid')
plot = sns.barplot(y = 'Expenditure' , x = 'Year',
data = leagues11, estimator = sum,
hue = 'League' ,ci = False, palette = sns.color_palette("husl", 5))
plot1 = plot.get_figure()
plt.xlabel('Year', size = 15)
plt.ylabel('Transfer Value (Million Euros)', size = 18)
plt.title('Money Spent by Europes Top 5 Leagues [2011 to 2022]', size = 20)
plot1.savefig("BarPlot.png")
sns.set(rc={'figure.figsize': (15, 8)})
sns.set_style('white')
sns.set(rc={'figure.figsize': (15, 8)})
sns.set_style('whitegrid')
plt.xticks(rotation=0, size=15)
plt.yticks(size=15)
sns.set_style('whitegrid')
sns.set(rc={'figure.figsize': (15, 8)})
sns.set(rc={'figure.figsize': (18, 8)})
sns.set_style('whitegrid')
sns.boxplot(data=leagues11, y='Expenditure', x='League', palette=sns.color_palette('husl', 5))
plt.xticks(rotation=0, size=15)
plt.yticks(size=15)
plt.xlabel('Leagues', size=15)
plt.ylabel('Transfer Value (Million Euros)', size=18)
plt.title('Box Plot : Money Spent by Different Leagues[2011 - 2022]', size=20) | code |
105171993/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes
leagues5 = inex.groupby(by=['League', 'Year'])['Expenditure', 'Arrivals', 'Income', 'Depatures', 'Balance'].sum()
leagues11 = inex[(inex['League'] == 'LaLiga') | (inex['League'] == 'Premier League') | (inex['League'] == 'Serie A') | (inex['League'] == 'Ligue 1') | (inex['League'] == 'Bundesliga')]
leagues11
#Number of Players Transfered per league last 12 years
f, ax1 = plt.subplots(figsize=(15, 8))
sns.set_style('whitegrid')
ax1 = sns.barplot(y = 'Arrivals' , x = 'Year',hue = 'League', data = leagues11,estimator = sum, ci = False,
palette = sns.color_palette("husl",5) )
ax1.bar_label(ax1.containers[2], color = 'black', size = 15)
#ax1.bar_label(ax1.containers[3], color = 'black', size = 9)
ax1.tick_params(labelsize = 15)
plt.xticks(rotation = 0, size = 15)
plt.yticks(size = 15)
plt.xlabel('Year', size = 18)
plt.ylabel('Number of Transfers', size = 18)
plt.title('Total Number of Players Transfered Per League [2011 to 2022]', size = 20)
#Bar Plot
import seaborn as sns
#palette = sns.color_palette(['darkblue','red','green','yellow','pink'])
sns.set(rc={'figure.figsize':(15,8)})
sns.set_style('whitegrid')
plot = sns.barplot(y = 'Expenditure' , x = 'Year',
data = leagues11, estimator = sum,
hue = 'League' ,ci = False, palette = sns.color_palette("husl", 5))
plot1 = plot.get_figure()
plt.xlabel('Year', size = 15)
plt.ylabel('Transfer Value (Million Euros)', size = 18)
plt.title('Money Spent by Europes Top 5 Leagues [2011 to 2022]', size = 20)
plot1.savefig("BarPlot.png")
sns.set(rc={'figure.figsize': (15, 8)})
sns.set_style('white')
sns.lmplot(y='Expenditure', x='Year', ci=None, data=leagues11[leagues11.League.isin(['Serie A', 'Premier League', 'LaLiga', 'Bundesliga', 'Ligue 1'])], hue='League', palette=sns.color_palette('husl', 5), col='League', line_kws={'color': 'black', 'lw': 5}, scatter_kws={'s': 200, 'edgecolor': 'black', 'alpha': 0.4})
plt.savefig('LMPlot.jpg') | code |
105171993/cell_10 | [
"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
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes
leagues5 = inex.groupby(by=['League', 'Year'])['Expenditure', 'Arrivals', 'Income', 'Depatures', 'Balance'].sum()
leagues11 = inex[(inex['League'] == 'LaLiga') | (inex['League'] == 'Premier League') | (inex['League'] == 'Serie A') | (inex['League'] == 'Ligue 1') | (inex['League'] == 'Bundesliga')]
leagues11
f, ax1 = plt.subplots(figsize=(15, 8))
sns.set_style('whitegrid')
ax1 = sns.barplot(y='Arrivals', x='Year', hue='League', data=leagues11, estimator=sum, ci=False, palette=sns.color_palette('husl', 5))
ax1.bar_label(ax1.containers[2], color='black', size=15)
ax1.tick_params(labelsize=15)
plt.xticks(rotation=0, size=15)
plt.yticks(size=15)
plt.xlabel('Year', size=18)
plt.ylabel('Number of Transfers', size=18)
plt.title('Total Number of Players Transfered Per League [2011 to 2022]', size=20) | code |
105171993/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
inex = pd.read_csv('../input/incomeexpendmerged/MergedNew.csv')
inex.dtypes
leagues5 = inex.groupby(by=['League', 'Year'])['Expenditure', 'Arrivals', 'Income', 'Depatures', 'Balance'].sum()
leagues11 = inex[(inex['League'] == 'LaLiga') | (inex['League'] == 'Premier League') | (inex['League'] == 'Serie A') | (inex['League'] == 'Ligue 1') | (inex['League'] == 'Bundesliga')]
leagues11
#Number of Players Transfered per league last 12 years
f, ax1 = plt.subplots(figsize=(15, 8))
sns.set_style('whitegrid')
ax1 = sns.barplot(y = 'Arrivals' , x = 'Year',hue = 'League', data = leagues11,estimator = sum, ci = False,
palette = sns.color_palette("husl",5) )
ax1.bar_label(ax1.containers[2], color = 'black', size = 15)
#ax1.bar_label(ax1.containers[3], color = 'black', size = 9)
ax1.tick_params(labelsize = 15)
plt.xticks(rotation = 0, size = 15)
plt.yticks(size = 15)
plt.xlabel('Year', size = 18)
plt.ylabel('Number of Transfers', size = 18)
plt.title('Total Number of Players Transfered Per League [2011 to 2022]', size = 20)
import seaborn as sns
sns.set(rc={'figure.figsize': (15, 8)})
sns.set_style('whitegrid')
plot = sns.barplot(y='Expenditure', x='Year', data=leagues11, estimator=sum, hue='League', ci=False, palette=sns.color_palette('husl', 5))
plot1 = plot.get_figure()
plt.xlabel('Year', size=15)
plt.ylabel('Transfer Value (Million Euros)', size=18)
plt.title('Money Spent by Europes Top 5 Leagues [2011 to 2022]', size=20)
plot1.savefig('BarPlot.png') | code |
18132466/cell_21 | [
"text_plain_output_1.png"
] | from scipy.linalg import eigh
from sklearn import decomposition
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
sample_data = standardized_data
covar_matrix = np.matmul(sample_data.T, sample_data)
from scipy.linalg import eigh
values, vectors = eigh(covar_matrix, eigvals=(782, 783))
vectors = vectors.T
import matplotlib.pyplot as plt
new_coordinates = np.matmul(vectors, sample_data.T)
import pandas as pd
new_coordinates = np.vstack((new_coordinates, labels)).T
dataframe = pd.DataFrame(data=new_coordinates, columns=('1st_principal', '2nd_principal', 'label'))
import seaborn as sn
sn.FacetGrid(dataframe, hue='label', size=6).map(plt.scatter, '1st_principal', '2nd_principal').add_legend()
from sklearn import decomposition
pca = decomposition.PCA()
pca.n_components = 2
pca_data = pca.fit_transform(sample_data)
pca_data = np.vstack((pca_data.T, labels)).T
pca_df = pd.DataFrame(data=pca_data, columns=('1st_principal', '2nd_principal', 'label'))
sn.FacetGrid(pca_df, hue='label', size=6).map(plt.scatter, '1st_principal', '2nd_principal').add_legend()
pca.n_components = 784
pca_data = pca.fit_transform(sample_data)
percentage_var_explained = pca.explained_variance_ / np.sum(pca.explained_variance_)
cum_var_explained = np.cumsum(percentage_var_explained)
plt.figure(1, figsize=(6, 4))
plt.clf()
plt.plot(cum_var_explained, linewidth=2)
plt.axis('tight')
plt.grid()
plt.xlabel('n_components')
plt.ylabel('Cumulative_explained_variance')
plt.show() | code |
18132466/cell_13 | [
"text_plain_output_1.png"
] | from scipy.linalg import eigh
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
sample_data = standardized_data
covar_matrix = np.matmul(sample_data.T, sample_data)
from scipy.linalg import eigh
values, vectors = eigh(covar_matrix, eigvals=(782, 783))
vectors = vectors.T
import matplotlib.pyplot as plt
new_coordinates = np.matmul(vectors, sample_data.T)
print('resultant new data points shape ', vectors.shape, 'X', sample_data.shape) | code |
18132466/cell_9 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
print(standardized_data.shape) | code |
18132466/cell_4 | [
"application_vnd.jupyter.stderr_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)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
print(d.shape)
print(l.shape) | code |
18132466/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18132466/cell_19 | [
"text_plain_output_1.png"
] | from scipy.linalg import eigh
from sklearn import decomposition
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
sample_data = standardized_data
covar_matrix = np.matmul(sample_data.T, sample_data)
from scipy.linalg import eigh
values, vectors = eigh(covar_matrix, eigvals=(782, 783))
vectors = vectors.T
import matplotlib.pyplot as plt
new_coordinates = np.matmul(vectors, sample_data.T)
import pandas as pd
new_coordinates = np.vstack((new_coordinates, labels)).T
dataframe = pd.DataFrame(data=new_coordinates, columns=('1st_principal', '2nd_principal', 'label'))
import seaborn as sn
sn.FacetGrid(dataframe, hue='label', size=6).map(plt.scatter, '1st_principal', '2nd_principal').add_legend()
from sklearn import decomposition
pca = decomposition.PCA()
pca.n_components = 2
pca_data = pca.fit_transform(sample_data)
pca_data = np.vstack((pca_data.T, labels)).T
pca_df = pd.DataFrame(data=pca_data, columns=('1st_principal', '2nd_principal', 'label'))
sn.FacetGrid(pca_df, hue='label', size=6).map(plt.scatter, '1st_principal', '2nd_principal').add_legend()
plt.show() | code |
18132466/cell_7 | [
"application_vnd.jupyter.stderr_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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
labels = l.head(15000)
data = d.head(15000)
print('the shape of sample data = ', data.shape) | code |
18132466/cell_18 | [
"text_plain_output_1.png"
] | from scipy.linalg import eigh
from sklearn import decomposition
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
sample_data = standardized_data
covar_matrix = np.matmul(sample_data.T, sample_data)
from scipy.linalg import eigh
values, vectors = eigh(covar_matrix, eigvals=(782, 783))
vectors = vectors.T
import matplotlib.pyplot as plt
new_coordinates = np.matmul(vectors, sample_data.T)
from sklearn import decomposition
pca = decomposition.PCA()
pca.n_components = 2
pca_data = pca.fit_transform(sample_data)
print('shape of pca_reduced.shape= ', pca_data.shape) | code |
18132466/cell_15 | [
"text_plain_output_1.png"
] | from scipy.linalg import eigh
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
sample_data = standardized_data
covar_matrix = np.matmul(sample_data.T, sample_data)
from scipy.linalg import eigh
values, vectors = eigh(covar_matrix, eigvals=(782, 783))
vectors = vectors.T
import matplotlib.pyplot as plt
new_coordinates = np.matmul(vectors, sample_data.T)
import pandas as pd
new_coordinates = np.vstack((new_coordinates, labels)).T
dataframe = pd.DataFrame(data=new_coordinates, columns=('1st_principal', '2nd_principal', 'label'))
import seaborn as sn
sn.FacetGrid(dataframe, hue='label', size=6).map(plt.scatter, '1st_principal', '2nd_principal').add_legend()
plt.show() | code |
18132466/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
print(d0.head(5)) | code |
18132466/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from scipy.linalg import eigh
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
sample_data = standardized_data
covar_matrix = np.matmul(sample_data.T, sample_data)
from scipy.linalg import eigh
values, vectors = eigh(covar_matrix, eigvals=(782, 783))
vectors = vectors.T
import matplotlib.pyplot as plt
new_coordinates = np.matmul(vectors, sample_data.T)
import pandas as pd
new_coordinates = np.vstack((new_coordinates, labels)).T
dataframe = pd.DataFrame(data=new_coordinates, columns=('1st_principal', '2nd_principal', 'label'))
print(dataframe.head()) | code |
18132466/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
sample_data = standardized_data
covar_matrix = np.matmul(sample_data.T, sample_data)
print('The shape of variance matrix = ', covar_matrix.shape) | code |
18132466/cell_12 | [
"text_plain_output_1.png"
] | from scipy.linalg import eigh
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
#Pick first 15k data-points to work on for time-efficiency.
#Exercise: Perform the same analysis on all of 42K data-point
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(data)
sample_data = standardized_data
covar_matrix = np.matmul(sample_data.T, sample_data)
from scipy.linalg import eigh
values, vectors = eigh(covar_matrix, eigvals=(782, 783))
print('Shape of eigen vectors = ', vectors.shape)
vectors = vectors.T
print('Updated shape of eigen vectors =', vectors.shape) | code |
18132466/cell_5 | [
"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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('../input/train.csv')
l = d0['label']
d = d0.drop('label', axis=1)
plt.figure(figsize=(7, 7))
idx = 150
grid_data = d.iloc[idx].as_matrix().reshape(28, 28)
plt.imshow(grid_data, interpolation='none', cmap='gray')
plt.show()
print(l[idx]) | code |
34119712/cell_4 | [
"text_plain_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/iris/Iris.csv')
df.head() | code |
34119712/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df.describe() | code |
34119712/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 |
34119712/cell_5 | [
"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/iris/Iris.csv')
df.info() | code |
18118019/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.applications.vgg19 import VGG19
from keras.layers import GlobalAveragePooling2D, Dropout, Dense, Conv2D
from keras.layers import MaxPooling2D, Flatten, Dense
from keras.models import Model, Sequential
from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.preprocessing.image import img_to_array
from keras.applications.vgg19 import preprocess_input
from keras.models import Model, Sequential
from keras.layers import GlobalAveragePooling2D, Dropout, Dense, Conv2D
from keras.layers import MaxPooling2D, Flatten, Dense
vgg19 = VGG19(weights='../input/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5', include_top=False, input_shape=(224, 224, 3))
for l in vgg19.layers:
if l is not None:
l.trainable = False
x = vgg19.output
x = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dense(256, activation='relu')(x)
predictions = Dense(5, activation='softmax')(x)
model = Model(inputs=vgg19.input, outputs=predictions)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | code |
18118019/cell_3 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input
from keras.layers import GlobalAveragePooling2D, Dropout, Dense, Conv2D
from keras.layers import MaxPooling2D, Flatten, Dense
from keras.models import Model, Sequential
from keras.preprocessing import image
from keras.preprocessing.image import img_to_array
import numpy as np
import pandas as pd
import numpy as np
from PIL import Image
import pandas as pd
import os
train = pd.read_csv('../input/aptos2019-blindness-detection/train.csv')
test = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')
submit = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')
diagnosis_encoded = pd.get_dummies(train.diagnosis)
from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.preprocessing.image import img_to_array
from keras.applications.vgg19 import preprocess_input
from keras.models import Model, Sequential
from keras.layers import GlobalAveragePooling2D, Dropout, Dense, Conv2D
from keras.layers import MaxPooling2D, Flatten, Dense
vgg19 = VGG19(weights='../input/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5', include_top=False, input_shape=(224, 224, 3))
for l in vgg19.layers:
if l is not None:
l.trainable = False
x = vgg19.output
x = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dense(256, activation='relu')(x)
predictions = Dense(5, activation='softmax')(x)
model = Model(inputs=vgg19.input, outputs=predictions)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
x_train_list = []
y_train_list = []
for index, row in train.iterrows():
my_pic_name = row.id_code
im = Image.open('../input/aptos2019-blindness-detection/train_images/' + my_pic_name + '.png')
im_224 = im.resize((224, 224), Image.ANTIALIAS)
image = img_to_array(im_224)
image = preprocess_input(image)
x_train_list.append(image)
y_train_list.append(diagnosis_encoded.loc[index])
x_train_raw = np.array(x_train_list, np.float32) / 255.0
y_train_raw = np.array(y_train_list, np.uint8)
if len(x_train_list) % 200 == 0:
model.train_on_batch(x_train_raw, y_train_raw)
x_train_list = []
y_train_list = []
print('train on batch ...') | code |
2010222/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
total = merged_dataset.isnull().sum().sort_values(ascending=False)
percent = (merged_dataset.isnull().sum() / merged_dataset.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
print(missing_data) | code |
2010222/cell_11 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import LinearRegression
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
total = merged_dataset.isnull().sum().sort_values(ascending=False)
percent = (merged_dataset.isnull().sum() / merged_dataset.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
corrmat = merged_dataset.corr()['SalePrice']
corrmat = corrmat.sort_values(axis=0, ascending=False)
corrmat[corrmat > 0.5]
merged_dataset.select_dtypes(include=['int64', 'float64']).columns
merged_dataset['Alley'].fillna('None', inplace=True)
merged_dataset['BsmtQual'].fillna(value='None', inplace=True)
merged_dataset['BsmtCond'].fillna(value='None', inplace=True)
merged_dataset['BsmtExposure'].fillna(value='None', inplace=True)
merged_dataset['BsmtFinType1'].fillna(value='None', inplace=True)
merged_dataset['BsmtFinType2'].fillna(value='None', inplace=True)
merged_dataset['BsmtFinSF1'].fillna(value=0, inplace=True)
merged_dataset['BsmtFinSF2'].fillna(value=0, inplace=True)
merged_dataset['BsmtFullBath'].fillna(value=0, inplace=True)
merged_dataset['BsmtHalfBath'].fillna(value=0, inplace=True)
merged_dataset['BsmtUnfSF'].fillna(value=0, inplace=True)
merged_dataset['TotalBsmtSF'].fillna(value=0, inplace=True)
merged_dataset['Electrical'].fillna(value='SBrkr', inplace=True)
merged_dataset['FireplaceQu'].fillna(value='None', inplace=True)
merged_dataset['GarageType'].fillna(value='None', inplace=True)
merged_dataset['GarageYrBlt'].fillna(value='None', inplace=True)
merged_dataset['GarageFinish'].fillna(value='None', inplace=True)
merged_dataset['GarageQual'].fillna(value='None', inplace=True)
merged_dataset['GarageCond'].fillna(value='None', inplace=True)
merged_dataset['GarageArea'].fillna(value=0, inplace=True)
merged_dataset['GarageCars'].fillna(value=0, inplace=True)
merged_dataset['PoolQC'].fillna(value='None', inplace=True)
merged_dataset['LotFrontage'].fillna(value=0, inplace=True)
merged_dataset['MiscFeature'].fillna(value='None', inplace=True)
merged_dataset['Exterior1st'].fillna(value='None', inplace=True)
merged_dataset['Exterior2nd'].fillna(value='None', inplace=True)
merged_dataset['Functional'].fillna(value='None', inplace=True)
merged_dataset['KitchenQual'].fillna(value='None', inplace=True)
merged_dataset['MSZoning'].fillna(value='None', inplace=True)
merged_dataset['SaleType'].fillna(value='None', inplace=True)
merged_dataset['Utilities'].fillna(value='None', inplace=True)
merged_dataset['MasVnrType'].fillna(value='None', inplace=True)
merged_dataset['MasVnrArea'].fillna(value=0, inplace=True)
merged_dataset['Fence'].fillna(value='None', inplace=True)
merged_dataset['SalePrice'] = np.log1p(merged_dataset['SalePrice'])
numeric_feats = merged_dataset.dtypes[merged_dataset.dtypes != 'object'].index
skewed_feats = merged_dataset[numeric_feats].apply(lambda x: skew(x.dropna()))
skewed_feats = skewed_feats[skewed_feats > 0.75]
skewed_feats = skewed_feats.index
merged_dataset[skewed_feats] = np.log1p(merged_dataset[skewed_feats])
new_train = merged_dataset[:1460]
X_train = new_train.drop('SalePrice', axis=1)
y_train = new_train['SalePrice']
new_test = merged_dataset[1460:]
X_test = new_test.drop('SalePrice', axis=1)
lr = LinearRegression().fit(X_train, y_train)
prediction = np.expm1(lr.predict(X_test))
print(prediction) | code |
2010222/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import skew
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.linear_model import LinearRegression
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
merged_dataset = pd.concat([train_data, test_data], axis=0) | code |
2010222/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
total = merged_dataset.isnull().sum().sort_values(ascending=False)
percent = (merged_dataset.isnull().sum() / merged_dataset.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
corrmat = merged_dataset.corr()['SalePrice']
corrmat = corrmat.sort_values(axis=0, ascending=False)
corrmat[corrmat > 0.5] | code |
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