path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
122260425/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
item_type_visibility = df[['Item_Type', 'Item_Visibility']].sort_values(by='Item_Visibility', ascending=False).reset_index()[['Item_Type', 'Item_Visibility']]
item_type_visibility = item_type_visibility[item_type_visibility['Item_Visibility'] != 0]
item_type_visibility_average = item_type_visibility.groupby('Item_Type').mean().sort_values(by='Item_Visibility', ascending=False).reset_index()
item_type_visibility_average | code |
122260425/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
item_type_visibility = df[['Item_Type', 'Item_Visibility']].sort_values(by='Item_Visibility', ascending=False).reset_index()[['Item_Type', 'Item_Visibility']]
item_type_visibility = item_type_visibility[item_type_visibility['Item_Visibility'] != 0]
display(item_type_visibility.head(10))
item_type_visibility.tail(10) | code |
122260425/cell_30 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
item_type_visibility = df[['Item_Type', 'Item_Visibility']].sort_values(by='Item_Visibility', ascending=False).reset_index()[['Item_Type', 'Item_Visibility']]
item_type_visibility = item_type_visibility[item_type_visibility['Item_Visibility'] != 0]
item_type_visibility_average = item_type_visibility.groupby('Item_Type').mean().sort_values(by='Item_Visibility', ascending=False).reset_index()
item_type_visibility_average
item_type_count = df.groupby('Item_Type').count()['Item_Identifier'].reset_index().rename(columns={'Item_Identifier': 'Number_Of_Items'}).sort_values(by='Number_Of_Items', ascending=False)
item_type_count
plt.figure(figsize=(25, 8))
sns.barplot(data=item_type_count, x='Item_Type', y='Number_Of_Items', palette='autumn')
plt.ylabel('Type of Item')
plt.xlabel('Number of Items')
plt.title('Items and their types')
plt.show() | code |
122260425/cell_33 | [
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
item_type_count = df.groupby('Item_Type').count()['Item_Identifier'].reset_index().rename(columns={'Item_Identifier': 'Number_Of_Items'}).sort_values(by='Number_Of_Items', ascending=False)
item_type_count
df_outlets = df['Outlet_Identifier'].value_counts().sort_index().reset_index().rename(columns={'index': 'Outlet_Identifier', 'Outlet_Identifier': 'Number_Of_Outlets'})
df_outlets.sort_values(by='Number_Of_Outlets', ascending=False) | code |
122260425/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
sns.barplot(data=df_itemfat, x='Fat_Content', y='Number_of_items', palette='autumn')
plt.xlabel('Type of item')
plt.ylabel('Number of items')
plt.title('Items with different fat contents')
plt.show() | code |
122260425/cell_6 | [
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
display(traindf.shape)
traindf.info() | code |
122260425/cell_29 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
item_type_count = df.groupby('Item_Type').count()['Item_Identifier'].reset_index().rename(columns={'Item_Identifier': 'Number_Of_Items'}).sort_values(by='Number_Of_Items', ascending=False)
item_type_count | code |
122260425/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
item_type_visibility = df[['Item_Type', 'Item_Visibility']].sort_values(by='Item_Visibility', ascending=False).reset_index()[['Item_Type', 'Item_Visibility']]
item_type_visibility = item_type_visibility[item_type_visibility['Item_Visibility'] != 0]
item_type_visibility_average = item_type_visibility.groupby('Item_Type').mean().sort_values(by='Item_Visibility', ascending=False).reset_index()
item_type_visibility_average
sns.barplot(data=item_type_visibility_average, y='Item_Type', x='Item_Visibility', palette='autumn', errorbar=None, orient='h')
plt.ylabel('Type of Item')
plt.xlabel('Visibility of item')
plt.title('Various items and the visibility')
plt.show() | code |
122260425/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.head()
df.tail()
df.isnull().sum() | code |
122260425/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat | code |
122260425/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import SVR
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, r2_score, mean_squared_error, mean_absolute_error
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122260425/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes | code |
122260425/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts() | code |
122260425/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3) | code |
122260425/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
sns.histplot(data=df, x='Item_Weight', kde=True, color='orange')
plt.show()
sns.barplot(y='Item_Weight', x='Number_Of_Items', data=df_itemwt_sorted, palette='autumn', order=df_itemwt_sorted['Item_Weight'], orient='h')
plt.ylabel('Weight of Items')
plt.xlabel('Number of Items')
plt.title('Items with different weights')
plt.show() | code |
122260425/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df['Item_Fat_Content'].value_counts() | code |
122260425/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
display(traindf.head())
display(testdf.head()) | code |
122260425/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
item_type_count = df.groupby('Item_Type').count()['Item_Identifier'].reset_index().rename(columns={'Item_Identifier': 'Number_Of_Items'}).sort_values(by='Number_Of_Items', ascending=False)
item_type_count
df.head() | code |
122260425/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted | code |
122260425/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')
testdf = pd.read_csv('/kaggle/input/bigmart-sales-data/Test.csv')
traindf.dtypes
traindf.describe().round(3)
traindf.isna().sum()
df = pd.concat([traindf, testdf], axis=0)
df.shape
df.isnull().sum()
df = df.dropna(subset=['Item_Weight', 'Outlet_Size', 'Item_Outlet_Sales'])
df_itemwt = df.Item_Weight.value_counts().reset_index().rename(columns={'index': 'Item_Weight', 'Item_Weight': 'Number_Of_Items'})
df_itemwt_sorted = df_itemwt.sort_values(by='Number_Of_Items', ascending=False).head(20).reset_index(drop=True)
df_itemwt_sorted
df.loc[df['Item_Fat_Content'] == 'LF', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'low fat', 'Item_Fat_Content'] = 'Low Fat'
df.loc[df['Item_Fat_Content'] == 'reg', 'Item_Fat_Content'] = 'Regular'
df['Item_Fat_Content'].value_counts()
df_itemfat = df.Item_Fat_Content.value_counts().reset_index().rename(columns={'index': 'Fat_Content', 'Item_Fat_Content': 'Number_of_items'})
df_itemfat
item_type_visibility = df[['Item_Type', 'Item_Visibility']].sort_values(by='Item_Visibility', ascending=False).reset_index()[['Item_Type', 'Item_Visibility']]
item_type_visibility.head() | code |
128030068/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum()
features = df.drop('Class', axis=1)
target = df['Class'].values
from sklearn.impute import SimpleImputer
nums_data = features.select_dtypes('number')
text_data = features.select_dtypes('object')
imputer = SimpleImputer(strategy='median')
nums_data_imp = imputer.fit_transform(nums_data)
text_data['Artist Name'].value_counts() | code |
128030068/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(max_iter=1000)
lr.fit(Xtrain, ytrain)
prediction = lr.predict(Xtest)
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
lr = OneVsRestClassifier(LogisticRegression(max_iter=1000))
lr.fit(Xtrain, ytrain) | code |
128030068/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
sns.countplot(data=df, x='Class')
plt.show() | code |
128030068/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(max_iter=1000)
lr.fit(Xtrain, ytrain) | code |
128030068/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.info() | code |
128030068/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.head(2) | code |
128030068/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum()
features = df.drop('Class', axis=1)
target = df['Class'].values
from sklearn.impute import SimpleImputer
nums_data = features.select_dtypes('number')
text_data = features.select_dtypes('object')
imputer = SimpleImputer(strategy='median')
nums_data_imp = imputer.fit_transform(nums_data)
nums_data_imp = pd.DataFrame(nums_data_imp, columns=imputer.get_feature_names_out())
nums_data_imp.isna().sum()
pd.Series(ytrain).value_counts() | code |
128030068/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum() | code |
128030068/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum() | code |
128030068/cell_15 | [
"image_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum()
features = df.drop('Class', axis=1)
target = df['Class'].values
from sklearn.impute import SimpleImputer
nums_data = features.select_dtypes('number')
text_data = features.select_dtypes('object')
imputer = SimpleImputer(strategy='median')
nums_data_imp = imputer.fit_transform(nums_data)
nums_data_imp = pd.DataFrame(nums_data_imp, columns=imputer.get_feature_names_out())
nums_data_imp.isna().sum()
nums_data_imp.head() | code |
128030068/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df['Class'].value_counts() | code |
128030068/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(max_iter=1000)
lr.fit(Xtrain, ytrain)
prediction = lr.predict(Xtest)
from sklearn.metrics import classification_report
print(classification_report(prediction, ytest)) | code |
128030068/cell_12 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape
df.isna().sum()
df.duplicated().sum()
features = df.drop('Class', axis=1)
target = df['Class'].values
from sklearn.impute import SimpleImputer
nums_data = features.select_dtypes('number')
text_data = features.select_dtypes('object')
imputer = SimpleImputer(strategy='median')
nums_data_imp = imputer.fit_transform(nums_data)
nums_data_imp = pd.DataFrame(nums_data_imp, columns=imputer.get_feature_names_out())
nums_data_imp.isna().sum() | code |
128030068/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/music-genre-classification/train.csv')
df.shape | code |
2034634/cell_6 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
def plot_analysis(ww):
for n in range(1, 7):
gga = scipy.ndimage.filters.gaussian_filter(ww, 2 * n * 1.0)
ggb = scipy.ndimage.filters.gaussian_filter(ww, n * 1.0)
xx = ggb - gga
mm = xx == scipy.ndimage.morphology.grey_dilation(xx, size=(3, 3))
plt.axis('off')
plt.axis('off')
plt.axis('off')
imgs = img_gen()
for k in range(10):
qq = next(imgs)
ww = qq.reshape(20, 20)
plt.figure(figsize=(10, 5))
plot_analysis(ww) | code |
2034634/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
def plot_analysis(ww):
for n in range(1, 7):
gga = scipy.ndimage.filters.gaussian_filter(ww, 2 * n * 1.0)
ggb = scipy.ndimage.filters.gaussian_filter(ww, n * 1.0)
xx = ggb - gga
mm = xx == scipy.ndimage.morphology.grey_dilation(xx, size=(3, 3))
plt.axis('off')
plt.axis('off')
plt.axis('off')
imgs = img_gen()
for k in range(10):
qq = next(imgs)
ww = qq.reshape(20, 20)
def find_dice(ww):
gga = scipy.ndimage.filters.gaussian_filter(ww, 4.0)
ggb = scipy.ndimage.filters.gaussian_filter(ww, 2.0)
ggb - gga
xx = ggb - gga
mm = xx == scipy.ndimage.morphology.grey_dilation(xx, size=(3, 3))
mm[0, :] = 0
mm[-1, :] = 0
mm[:, 0] = 0
mm[:, -1] = 0
return np.nonzero(mm)
plt.figure(figsize=(15, 8))
imgs = img_gen()
for k in range(50):
qq = next(imgs)
ww = qq.reshape(20, 20)
plt.subplot(5, 10, k + 1)
plt.imshow(ww)
plt.axis('off')
for y, x in zip(*find_dice(ww)):
plt.plot(x, y, 'ro') | code |
129011803/cell_9 | [
"text_plain_output_1.png"
] | import librosa
from scipy.stats import skew
from scipy.stats import kurtosis | code |
129011803/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
sounds_freq = sounds_df['class'].value_counts().sort_values()
print(sounds_freq) | code |
129011803/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
folds_freq = sounds_df['fold'].value_counts().sort_index()
print(folds_freq) | code |
129011803/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
folds_freq = sounds_df['fold'].value_counts().sort_index()
folds_freq.plot(kind='pie', figsize=(5, 5), title='Folds', autopct='%1.1f%%', shadow=False, fontsize=8) | code |
129011803/cell_8 | [
"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)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
plt.figure(figsize=[25, 10])
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_counts()
plt.subplot(2, 5, i)
fold_freq.plot(kind='pie', title=f'fold {i}', autopct='%1.1f%%', shadow=False, fontsize=8) | code |
129011803/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import kurtosis
from scipy.stats import skew
import librosa
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_counts()
def get_mfcc(filename, fold):
wave, sr = librosa.load(f'../input/urbansound8k/fold{fold}/{filename}', mono=True, sr=22050)
wave = librosa.util.normalize(wave)
mfccs = librosa.feature.mfcc(y=wave, sr=sr, n_mfcc=40, hop_length=int(0.0232 * sr / 2.0), n_fft=int(0.0232 * sr))
mfccs_min = mfccs.min(axis=1)
mfccs_max = mfccs.max(axis=1)
mfccs_median = np.median(mfccs, axis=1)
mfccs_mean = np.mean(mfccs, axis=1)
mfccs_var = np.var(mfccs, axis=1)
mfccs_skewness = skew(mfccs, axis=1)
mfccs_kurtosis = kurtosis(mfccs, axis=1)
mfccs_first_derivative = np.diff(mfccs, n=1, axis=1)
mfccs_first_derivative_mean = np.mean(mfccs_first_derivative, axis=1)
mfccs_first_derivative_var = np.var(mfccs_first_derivative, axis=1)
mfccs_second_derivative = np.diff(mfccs, n=2, axis=1)
mfccs_second_derivative_mean = np.mean(mfccs_second_derivative, axis=1)
mfccs_second_derivative_var = np.var(mfccs_second_derivative, axis=1)
mfccs_stats = np.vstack((mfccs_min, mfccs_max, mfccs_median, mfccs_mean, mfccs_var, mfccs_skewness, mfccs_kurtosis, mfccs_first_derivative_mean, mfccs_first_derivative_var, mfccs_second_derivative_mean, mfccs_second_derivative_var))
return pd.Series([mfccs, mfccs_stats.transpose()])
plt.tight_layout()
plt.figure(figsize=[15, 10])
for i in range(0, 9):
ax = plt.subplot(3, 3, i + 1)
img = librosa.display.specshow(sounds_df['mfccs'][i], x_axis='time')
ax.set(title=sounds_df['class'][i])
plt.colorbar()
plt.tight_layout() | code |
129011803/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_counts()
plt.tight_layout()
sounds_df.head() | code |
129011803/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
sounds_df.head() | code |
129011803/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_counts()
plt.tight_layout()
max_length = sounds_df['mfccs_stats'][0].shape
print(max_length) | code |
129011803/cell_14 | [
"text_plain_output_1.png"
] | from scipy.stats import kurtosis
from scipy.stats import skew
from tqdm import tqdm
import librosa
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_counts()
def get_mfcc(filename, fold):
wave, sr = librosa.load(f'../input/urbansound8k/fold{fold}/{filename}', mono=True, sr=22050)
wave = librosa.util.normalize(wave)
mfccs = librosa.feature.mfcc(y=wave, sr=sr, n_mfcc=40, hop_length=int(0.0232 * sr / 2.0), n_fft=int(0.0232 * sr))
mfccs_min = mfccs.min(axis=1)
mfccs_max = mfccs.max(axis=1)
mfccs_median = np.median(mfccs, axis=1)
mfccs_mean = np.mean(mfccs, axis=1)
mfccs_var = np.var(mfccs, axis=1)
mfccs_skewness = skew(mfccs, axis=1)
mfccs_kurtosis = kurtosis(mfccs, axis=1)
mfccs_first_derivative = np.diff(mfccs, n=1, axis=1)
mfccs_first_derivative_mean = np.mean(mfccs_first_derivative, axis=1)
mfccs_first_derivative_var = np.var(mfccs_first_derivative, axis=1)
mfccs_second_derivative = np.diff(mfccs, n=2, axis=1)
mfccs_second_derivative_mean = np.mean(mfccs_second_derivative, axis=1)
mfccs_second_derivative_var = np.var(mfccs_second_derivative, axis=1)
mfccs_stats = np.vstack((mfccs_min, mfccs_max, mfccs_median, mfccs_mean, mfccs_var, mfccs_skewness, mfccs_kurtosis, mfccs_first_derivative_mean, mfccs_first_derivative_var, mfccs_second_derivative_mean, mfccs_second_derivative_var))
return pd.Series([mfccs, mfccs_stats.transpose()])
plt.tight_layout()
tqdm.pandas()
sounds_df[['mfccs', 'mfccs_stats']] = sounds_df[['slice_file_name', 'fold']].progress_apply(lambda x: get_mfcc(*x), axis=1) | code |
129011803/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
import matplotlib.pyplot as plt
for i in range(1, 11):
fold_df = sounds_df[sounds_df['fold'] == i]
fold_freq = fold_df['class'].value_counts()
sounds_df.plot.hist(bins=10, column=['duration'], by='class', figsize=(5, 20))
plt.tight_layout() | code |
129011803/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sounds_df = pd.read_csv('/kaggle/input/urbansound8k/UrbanSound8K.csv')
sounds_freq = sounds_df['class'].value_counts().sort_values()
sounds_freq.plot(kind='pie', figsize=(5, 5), title='Sounds', autopct='%1.1f%%', shadow=False, fontsize=8) | code |
121148889/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
import seaborn as sns
def plot(dataframe, col):
pass
train_df = train_df.drop('id', axis=1)
for col in train_df.select_dtypes('object'):
plot(train_df, col) | code |
121148889/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
print(train_df.describe(include='number')) | code |
121148889/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
print(train_df.head()) | code |
121148889/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
import seaborn as sns
def plot(dataframe, col):
pass
train_df = train_df.drop('id', axis=1)
df_copy = df_copy.drop('id', axis=1)
dataplot = sns.heatmap(df_copy.corr())
plt.show() | code |
121148889/cell_6 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
from sklearn.inspection import PartialDependenceDisplay
from lightgbm import LGBMRegressor | code |
121148889/cell_26 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"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
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
import seaborn as sns
def plot(dataframe, col):
pass
train_df = train_df.drop('id', axis=1)
test_df = test_df.drop('id', axis=1)
train_y = train_df['price']
train_df = train_df.drop(['price'], axis=1)
print(train_df) | code |
121148889/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
import seaborn as sns
def plot(dataframe, col):
sns.histplot(dataframe[col], bins=30, kde=True)
plt.show()
train_df = train_df.drop('id', axis=1)
for col in train_df.select_dtypes('number'):
plot(train_df, col) | code |
121148889/cell_28 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
import seaborn as sns
def plot(dataframe, col):
pass
train_df = train_df.drop('id', axis=1)
test_df = test_df.drop('id', axis=1)
train_y = train_df['price']
train_df = train_df.drop(['price'], axis=1)
encoder = OneHotEncoder(handle_unknown='ignore')
OH_cols_train = pd.DataFrame(encoder.fit_transform(train_df[['cut', 'color', 'clarity']]).toarray())
OH_cols_valid = pd.DataFrame(encoder.transform(test_df[['cut', 'color', 'clarity']]).toarray())
test_df = test_df.join(OH_cols_valid)
train_df = train_df.join(OH_cols_train)
train_df = train_df.drop(['cut', 'color', 'clarity'], axis=1)
test_df = test_df.drop(['cut', 'color', 'clarity'], axis=1)
print(train_df) | code |
121148889/cell_38 | [
"image_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.inspection import PartialDependenceDisplay
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
import seaborn as sns
def plot(dataframe, col):
pass
train_df = train_df.drop('id', axis=1)
test_df = test_df.drop('id', axis=1)
train_y = train_df['price']
train_df = train_df.drop(['price'], axis=1)
encoder = OneHotEncoder(handle_unknown='ignore')
OH_cols_train = pd.DataFrame(encoder.fit_transform(train_df[['cut', 'color', 'clarity']]).toarray())
OH_cols_valid = pd.DataFrame(encoder.transform(test_df[['cut', 'color', 'clarity']]).toarray())
test_df = test_df.join(OH_cols_valid)
train_df = train_df.join(OH_cols_train)
train_df = train_df.drop(['cut', 'color', 'clarity'], axis=1)
test_df = test_df.drop(['cut', 'color', 'clarity'], axis=1)
df_copy = df_copy.drop('id', axis=1)
dataplot=sns.heatmap(df_copy.corr())
plt.show()
X_train, X_test, y_train, y_test = train_test_split(train_df, train_y, test_size=0.2, random_state=42)
lgbm = LGBMRegressor(random_state=10, n_estimators=2730, reg_alpha=8.432915874559963, reg_lambda=1.140459608805678, colsample_bytree=0.5820085284323611, subsample=0.3005306113635547, learning_rate=0.003848337624183948, max_depth=82, num_leaves=60, min_child_samples=43)
lgbm.fit(X_train, y_train)
def PartialDependence(model, X_test, feature_name):
PartialDependenceDisplay.from_estimator(model, X_test, feature_name)
plt.show()
PartialDependence(lgbm, X_test, 'y')
PartialDependence(lgbm, X_test, 'x') | code |
121148889/cell_35 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
import seaborn as sns
def plot(dataframe, col):
pass
train_df = train_df.drop('id', axis=1)
test_df = test_df.drop('id', axis=1)
train_y = train_df['price']
train_df = train_df.drop(['price'], axis=1)
encoder = OneHotEncoder(handle_unknown='ignore')
OH_cols_train = pd.DataFrame(encoder.fit_transform(train_df[['cut', 'color', 'clarity']]).toarray())
OH_cols_valid = pd.DataFrame(encoder.transform(test_df[['cut', 'color', 'clarity']]).toarray())
test_df = test_df.join(OH_cols_valid)
train_df = train_df.join(OH_cols_train)
train_df = train_df.drop(['cut', 'color', 'clarity'], axis=1)
test_df = test_df.drop(['cut', 'color', 'clarity'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(train_df, train_y, test_size=0.2, random_state=42)
lgbm = LGBMRegressor(random_state=10, n_estimators=2730, reg_alpha=8.432915874559963, reg_lambda=1.140459608805678, colsample_bytree=0.5820085284323611, subsample=0.3005306113635547, learning_rate=0.003848337624183948, max_depth=82, num_leaves=60, min_child_samples=43)
lgbm.fit(X_train, y_train) | code |
121148889/cell_43 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
import seaborn as sns
def plot(dataframe, col):
pass
train_df = train_df.drop('id', axis=1)
test_df = test_df.drop('id', axis=1)
train_y = train_df['price']
train_df = train_df.drop(['price'], axis=1)
encoder = OneHotEncoder(handle_unknown='ignore')
OH_cols_train = pd.DataFrame(encoder.fit_transform(train_df[['cut', 'color', 'clarity']]).toarray())
OH_cols_valid = pd.DataFrame(encoder.transform(test_df[['cut', 'color', 'clarity']]).toarray())
test_df = test_df.join(OH_cols_valid)
train_df = train_df.join(OH_cols_train)
train_df = train_df.drop(['cut', 'color', 'clarity'], axis=1)
test_df = test_df.drop(['cut', 'color', 'clarity'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(train_df, train_y, test_size=0.2, random_state=42)
lgbm = LGBMRegressor(random_state=10, n_estimators=2730, reg_alpha=8.432915874559963, reg_lambda=1.140459608805678, colsample_bytree=0.5820085284323611, subsample=0.3005306113635547, learning_rate=0.003848337624183948, max_depth=82, num_leaves=60, min_child_samples=43)
lgbm.fit(X_train, y_train)
prediction = lgbm.predict(test_df)
print(prediction) | code |
121148889/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/playground-series-s3e8/train.csv'
test_path = '/kaggle/input/playground-series-s3e8/test.csv'
train_df = pd.DataFrame(pd.read_csv(train_path))
test_df = pd.DataFrame(pd.read_csv(test_path))
ids = test_df[['id']]
df_copy = train_df
print(train_df.describe(include='object')) | code |
16123550/cell_4 | [
"text_plain_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
train_path = '../input/train/train'
test_path = '../input/test/test'
label_frame = pd.read_csv('../input/train.csv')
test_frame = pd.read_csv('../input/sample_submission.csv')
x_train = []
x_test = []
y_train = np.array(label_frame['has_cactus'])
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
for fname in label_frame['id']:
image_path = os.path.join(train_path, fname)
pil_image = image.load_img(image_path, target_size=(32, 32, 3))
np_image = image.img_to_array(pil_image)
x_train.append(np_image)
for fname in test_frame['id']:
image_path = os.path.join(test_path, fname)
pil_image = image.load_img(image_path, target_size=(32, 32, 3))
np_image = image.img_to_array(pil_image)
x_test.append(np_image)
x_train = np.array(x_train)
x_train = x_train.astype('float32') / 255
x_test = np.array(x_test)
x_test = x_test.astype('float32') / 255
augmentations = ImageDataGenerator(vertical_flip=True, horizontal_flip=True, zoom_range=0.1)
augmentations.fit(x_train)
from keras.applications import VGG16
from keras import models
from keras import layers
from keras import optimizers
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
def get_model():
base = VGG16(include_top=False, weights='imagenet', input_shape=(32, 32, 3))
base.trainable = True
base.summary()
set_trainable = False
for layer in base.layers:
if layer.name == 'block5_conv3':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
model = models.Sequential()
model.add(base)
model.add(layers.Flatten())
model.add(layers.BatchNormalization())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])
return model
model = get_model()
"\nx_val = x_train[16600:]\ny_val = y_train[16600:]\n\nmodel_check_point = ModelCheckpoint('./model.h5',monitor = 'val_loss',save_best_only = True)\nearly_stopping = EarlyStopping(monitor = 'val_loss',patience = 25)\nreduce_lr_on_plateau = ReduceLROnPlateau(monitor = 'val_loss',patience = 15)\n\nhistory = model.fit(x_train[:16600],y_train[:16600],epochs = 80,batch_size = 250,validation_data = (x_val,y_val), callbacks = [model_check_point,reduce_lr_on_plateau])\n#visualize\nimport matplotlib.pyplot as plt\n\nacc = history.history['acc']\nval_acc = history.history['val_acc']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(len(acc))\n\nplt.plot(epochs, acc, 'bo', label='Training acc')\nplt.plot(epochs, val_acc, 'b', label='Validation acc')\nplt.title('Training and validation accuracy')\nplt.legend()\n\nplt.figure()\n\nplt.plot(epochs, loss, 'bo', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('Training and validation loss')\nplt.legend()\n\nplt.show()\n"
model.fit_generator(augmentations.flow(x_train, y_train), epochs=100, steps_per_epoch=250)
y_predictions = model.predict(x_test)
result = pd.DataFrame({'id': pd.read_csv('../input/sample_submission.csv')['id'], 'has_cactus': y_predictions.squeeze()})
result.to_csv('submissionMax.csv', index=False, columns=['id', 'has_cactus'])
print('submit successful') | code |
16123550/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing import image
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
train_path = '../input/train/train'
test_path = '../input/test/test'
label_frame = pd.read_csv('../input/train.csv')
test_frame = pd.read_csv('../input/sample_submission.csv')
x_train = []
x_test = []
y_train = np.array(label_frame['has_cactus'])
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
for fname in label_frame['id']:
image_path = os.path.join(train_path, fname)
pil_image = image.load_img(image_path, target_size=(32, 32, 3))
np_image = image.img_to_array(pil_image)
x_train.append(np_image)
for fname in test_frame['id']:
image_path = os.path.join(test_path, fname)
pil_image = image.load_img(image_path, target_size=(32, 32, 3))
np_image = image.img_to_array(pil_image)
x_test.append(np_image)
x_train = np.array(x_train)
x_train = x_train.astype('float32') / 255
x_test = np.array(x_test)
x_test = x_test.astype('float32') / 255
print(x_train.shape)
print(x_test.shape) | code |
16123550/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from keras.applications import VGG16
from keras import models
from keras import layers
from keras import optimizers
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
def get_model():
base = VGG16(include_top=False, weights='imagenet', input_shape=(32, 32, 3))
base.trainable = True
base.summary()
set_trainable = False
for layer in base.layers:
if layer.name == 'block5_conv3':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
model = models.Sequential()
model.add(base)
model.add(layers.Flatten())
model.add(layers.BatchNormalization())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])
return model
model = get_model()
"\nx_val = x_train[16600:]\ny_val = y_train[16600:]\nmodel_check_point = ModelCheckpoint('./model.h5',monitor = 'val_loss',save_best_only = True)\nearly_stopping = EarlyStopping(monitor = 'val_loss',patience = 25)\nreduce_lr_on_plateau = ReduceLROnPlateau(monitor = 'val_loss',patience = 15)\nhistory = model.fit(x_train[:16600],y_train[:16600],epochs = 80,batch_size = 250,validation_data = (x_val,y_val), callbacks = [model_check_point,reduce_lr_on_plateau])\n#visualize\nimport matplotlib.pyplot as plt\nacc = history.history['acc']\nval_acc = history.history['val_acc']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\nepochs = range(len(acc))\nplt.plot(epochs, acc, 'bo', label='Training acc')\nplt.plot(epochs, val_acc, 'b', label='Validation acc')\nplt.title('Training and validation accuracy')\nplt.legend()\nplt.figure()\nplt.plot(epochs, loss, 'bo', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('Training and validation loss')\nplt.legend()\nplt.show()\n" | code |
18153040/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.info() | code |
18153040/cell_25 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.Year = pd.to_datetime(total_rape['Year'], format='%Y').dt.strftime('%Y')
total_rape['Total_Rape_per_Year'] = total_rape.groupby('Year')['Victims_of_Rape_Total'].transform('sum')
plot_total_rape = total_rape.drop_duplicates('Year', keep='first', inplace=False)
plot_total_rape
total_rape | code |
18153040/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.head() | code |
18153040/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.Year = pd.to_datetime(total_rape['Year'], format='%Y').dt.strftime('%Y')
total_rape['Total_Rape_per_Year'] = total_rape.groupby('Year')['Victims_of_Rape_Total'].transform('sum')
plot_total_rape = total_rape.drop_duplicates('Year', keep='first', inplace=False)
plot_total_rape
plt.figure(figsize=(15, 5))
x = plot_total_rape['Year']
y = plot_total_rape['Total_Rape_per_Year']
plt.plot(x, y)
plt.title('Number of rapes per year in all of India for the decade (2001 - 2010)')
plt.xlabel('Year')
plt.ylabel('Total_Rape_per_Year')
plt.grid(True)
plt.show() | code |
18153040/cell_26 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.Year = pd.to_datetime(total_rape['Year'], format='%Y').dt.strftime('%Y')
total_rape['Total_Rape_per_Year'] = total_rape.groupby('Year')['Victims_of_Rape_Total'].transform('sum')
plot_total_rape = total_rape.drop_duplicates('Year', keep='first', inplace=False)
plot_total_rape
total_rape['Total_Rape_per_Area'] = total_rape.groupby('Area_Name')['Victims_of_Rape_Total'].transform('sum')
plot_total_area_rape = total_rape.drop_duplicates('Area_Name', keep='first', inplace=False) | code |
18153040/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
18153040/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns | code |
18153040/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.describe() | code |
18153040/cell_28 | [
"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)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.Year = pd.to_datetime(total_rape['Year'], format='%Y').dt.strftime('%Y')
total_rape['Total_Rape_per_Year'] = total_rape.groupby('Year')['Victims_of_Rape_Total'].transform('sum')
plot_total_rape = total_rape.drop_duplicates('Year', keep='first', inplace=False)
plot_total_rape
x = plot_total_rape['Year']
y = plot_total_rape['Total_Rape_per_Year']
total_rape['Total_Rape_per_Area'] = total_rape.groupby('Area_Name')['Victims_of_Rape_Total'].transform('sum')
plot_total_area_rape = total_rape.drop_duplicates('Area_Name', keep='first', inplace=False)
plt.figure(figsize=(75, 25))
x = plot_total_area_rape['Area_Name']
y = plot_total_area_rape['Total_Rape_per_Area']
plt.plot(x, y)
plt.title('Number of rapes in different parts of India for the decade (2001 - 2010)')
plt.xlabel('Name of Areas')
plt.ylabel('Total Rape per Area')
plt.grid(True)
plt.show() | code |
18153040/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.head() | code |
18153040/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum()
total_rape = rape_victim[rape_victim['Subgroup'] == 'Total Rape Victims']
total_rape.Year = pd.to_datetime(total_rape['Year'], format='%Y').dt.strftime('%Y')
total_rape['Total_Rape_per_Year'] = total_rape.groupby('Year')['Victims_of_Rape_Total'].transform('sum')
plot_total_rape = total_rape.drop_duplicates('Year', keep='first', inplace=False)
plot_total_rape | code |
18153040/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('') | code |
18153040/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
rape_victim = pd.read_csv('../input/20_Victims_of_rape.csv', na_filter='False')
rape_victim.columns
rape_victim.fillna('')
rape_victim.isnull().sum().sum() | code |
16157191/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn import metrics
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train = pd.read_csv('../input/cactus-images-csv/dataset/train.csv')
test = pd.read_csv('../input/cactus-images-csv/dataset/test.csv')
train = train.drop('Unnamed: 0', axis=1)
test = test.drop('Unnamed: 0', axis=1)
import keras
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, BatchNormalization, concatenate, AveragePooling2D
from keras.optimizers import Adam
def conv_layer(conv_x, filters):
conv_x = BatchNormalization()(conv_x)
conv_x = Activation('relu')(conv_x)
conv_x = Conv2D(filters, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(conv_x)
conv_x = Dropout(0.2)(conv_x)
return conv_x
def dense_block(block_x, filters, growth_rate, layers_in_block):
for i in range(layers_in_block):
each_layer = conv_layer(block_x, growth_rate)
block_x = concatenate([block_x, each_layer], axis=-1)
filters += growth_rate
return (block_x, filters)
def transition_block(trans_x, tran_filters):
trans_x = BatchNormalization()(trans_x)
trans_x = Activation('relu')(trans_x)
trans_x = Conv2D(tran_filters, (1, 1), kernel_initializer='he_uniform', padding='same', use_bias=False)(trans_x)
trans_x = AveragePooling2D((2, 2), strides=(2, 2))(trans_x)
return (trans_x, tran_filters)
def dense_net(filters, growth_rate, classes, dense_block_size, layers_in_block):
input_img = Input(shape=(32, 32, 3))
x = Conv2D(24, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(input_img)
dense_x = BatchNormalization()(x)
dense_x = Activation('relu')(x)
dense_x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(dense_x)
for block in range(dense_block_size - 1):
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x, filters = transition_block(dense_x, filters)
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x = BatchNormalization()(dense_x)
dense_x = Activation('relu')(dense_x)
dense_x = GlobalAveragePooling2D()(dense_x)
output = Dense(classes, activation='softmax')(dense_x)
return Model(input_img, output)
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
images = train.drop('label', axis=1)
images = np.asarray(images)
images = images.reshape(images.shape[0], 32, 32, 3)
label = train['label']
X_train, X_test, y_train, y_test = train_test_split(images, label.values, test_size=0.33)
Cat_test_y = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
dense_block_size = 3
layers_in_block = 4
growth_rate = 12
classes = 2
model = dense_net(growth_rate * 2, growth_rate, classes, dense_block_size, layers_in_block)
model.summary()
batch_size = 32
epochs = 10
optimizer = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=True, validation_data=(X_test, Cat_test_y))
import sys
import matplotlib
sys.stdout.flush()
matplotlib.use('Agg')
matplotlib.pyplot.style.use('ggplot')
N = epochs
from sklearn import metrics
label_pred = model.predict(X_test)
pred = []
for i in range(len(label_pred)):
pred.append(np.argmax(label_pred[i]))
Y_test = np.argmax(Cat_test_y, axis=1)
from sklearn import metrics
label_pred = model.predict(X_test)
pred = []
for i in range(len(label_pred)):
pred.append(np.argmax(label_pred[i]))
Y_test = np.argmax(Cat_test_y, axis=1)
print(metrics.accuracy_score(Y_test, pred)) | code |
16157191/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
print('Number of samples: ', len(df))
print('Number of Labels: ', np.unique(df.has_cactus)) | code |
16157191/cell_23 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train = pd.read_csv('../input/cactus-images-csv/dataset/train.csv')
test = pd.read_csv('../input/cactus-images-csv/dataset/test.csv')
train = train.drop('Unnamed: 0', axis=1)
test = test.drop('Unnamed: 0', axis=1)
import keras
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, BatchNormalization, concatenate, AveragePooling2D
from keras.optimizers import Adam
def conv_layer(conv_x, filters):
conv_x = BatchNormalization()(conv_x)
conv_x = Activation('relu')(conv_x)
conv_x = Conv2D(filters, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(conv_x)
conv_x = Dropout(0.2)(conv_x)
return conv_x
def dense_block(block_x, filters, growth_rate, layers_in_block):
for i in range(layers_in_block):
each_layer = conv_layer(block_x, growth_rate)
block_x = concatenate([block_x, each_layer], axis=-1)
filters += growth_rate
return (block_x, filters)
def transition_block(trans_x, tran_filters):
trans_x = BatchNormalization()(trans_x)
trans_x = Activation('relu')(trans_x)
trans_x = Conv2D(tran_filters, (1, 1), kernel_initializer='he_uniform', padding='same', use_bias=False)(trans_x)
trans_x = AveragePooling2D((2, 2), strides=(2, 2))(trans_x)
return (trans_x, tran_filters)
def dense_net(filters, growth_rate, classes, dense_block_size, layers_in_block):
input_img = Input(shape=(32, 32, 3))
x = Conv2D(24, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(input_img)
dense_x = BatchNormalization()(x)
dense_x = Activation('relu')(x)
dense_x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(dense_x)
for block in range(dense_block_size - 1):
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x, filters = transition_block(dense_x, filters)
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x = BatchNormalization()(dense_x)
dense_x = Activation('relu')(dense_x)
dense_x = GlobalAveragePooling2D()(dense_x)
output = Dense(classes, activation='softmax')(dense_x)
return Model(input_img, output)
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
images = train.drop('label', axis=1)
images = np.asarray(images)
images = images.reshape(images.shape[0], 32, 32, 3)
label = train['label']
X_train, X_test, y_train, y_test = train_test_split(images, label.values, test_size=0.33)
Cat_test_y = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
dense_block_size = 3
layers_in_block = 4
growth_rate = 12
classes = 2
model = dense_net(growth_rate * 2, growth_rate, classes, dense_block_size, layers_in_block)
model.summary()
batch_size = 32
epochs = 10
optimizer = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=True, validation_data=(X_test, Cat_test_y))
import sys
import matplotlib
sys.stdout.flush()
matplotlib.use('Agg')
matplotlib.pyplot.style.use('ggplot')
N = epochs
from sklearn import metrics
label_pred = model.predict(X_test)
pred = []
for i in range(len(label_pred)):
pred.append(np.argmax(label_pred[i]))
Y_test = np.argmax(Cat_test_y, axis=1)
print(metrics.classification_report(Y_test, pred)) | code |
16157191/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import matplotlib
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train = pd.read_csv('../input/cactus-images-csv/dataset/train.csv')
test = pd.read_csv('../input/cactus-images-csv/dataset/test.csv')
train = train.drop('Unnamed: 0', axis=1)
test = test.drop('Unnamed: 0', axis=1)
import keras
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, BatchNormalization, concatenate, AveragePooling2D
from keras.optimizers import Adam
def conv_layer(conv_x, filters):
conv_x = BatchNormalization()(conv_x)
conv_x = Activation('relu')(conv_x)
conv_x = Conv2D(filters, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(conv_x)
conv_x = Dropout(0.2)(conv_x)
return conv_x
def dense_block(block_x, filters, growth_rate, layers_in_block):
for i in range(layers_in_block):
each_layer = conv_layer(block_x, growth_rate)
block_x = concatenate([block_x, each_layer], axis=-1)
filters += growth_rate
return (block_x, filters)
def transition_block(trans_x, tran_filters):
trans_x = BatchNormalization()(trans_x)
trans_x = Activation('relu')(trans_x)
trans_x = Conv2D(tran_filters, (1, 1), kernel_initializer='he_uniform', padding='same', use_bias=False)(trans_x)
trans_x = AveragePooling2D((2, 2), strides=(2, 2))(trans_x)
return (trans_x, tran_filters)
def dense_net(filters, growth_rate, classes, dense_block_size, layers_in_block):
input_img = Input(shape=(32, 32, 3))
x = Conv2D(24, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(input_img)
dense_x = BatchNormalization()(x)
dense_x = Activation('relu')(x)
dense_x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(dense_x)
for block in range(dense_block_size - 1):
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x, filters = transition_block(dense_x, filters)
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x = BatchNormalization()(dense_x)
dense_x = Activation('relu')(dense_x)
dense_x = GlobalAveragePooling2D()(dense_x)
output = Dense(classes, activation='softmax')(dense_x)
return Model(input_img, output)
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
images = train.drop('label', axis=1)
images = np.asarray(images)
images = images.reshape(images.shape[0], 32, 32, 3)
label = train['label']
X_train, X_test, y_train, y_test = train_test_split(images, label.values, test_size=0.33)
Cat_test_y = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
dense_block_size = 3
layers_in_block = 4
growth_rate = 12
classes = 2
model = dense_net(growth_rate * 2, growth_rate, classes, dense_block_size, layers_in_block)
model.summary()
batch_size = 32
epochs = 10
optimizer = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=True, validation_data=(X_test, Cat_test_y))
import sys
import matplotlib
print('Generating plots...')
sys.stdout.flush()
matplotlib.use('Agg')
matplotlib.pyplot.style.use('ggplot')
matplotlib.pyplot.figure()
N = epochs
matplotlib.pyplot.plot(np.arange(0, N), history.history['loss'], label='train_loss')
matplotlib.pyplot.plot(np.arange(0, N), history.history['val_loss'], label='val_loss')
matplotlib.pyplot.plot(np.arange(0, N), history.history['acc'], label='train_acc')
matplotlib.pyplot.plot(np.arange(0, N), history.history['val_acc'], label='val_acc')
matplotlib.pyplot.title('Cactus Image Classification')
matplotlib.pyplot.xlabel('Epoch #')
matplotlib.pyplot.ylabel('Loss/Accuracy')
matplotlib.pyplot.legend(loc='lower left')
matplotlib.pyplot.savefig('plot.png') | code |
16157191/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
sns.distplot(df.has_cactus) | code |
16157191/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16157191/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train = pd.read_csv('../input/cactus-images-csv/dataset/train.csv')
test = pd.read_csv('../input/cactus-images-csv/dataset/test.csv')
print('TRAIN---------------------')
print('Shape: {}'.format(train.shape))
train = train.drop('Unnamed: 0', axis=1)
test = test.drop('Unnamed: 0', axis=1)
print('Label 0 (False): {}'.format(np.sum(train.label == 0)))
print('Label 1 (True): {}'.format(np.sum(train.label == 1)))
print('TEST----------------------')
print('Shape: {}'.format(test.shape)) | code |
16157191/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train = pd.read_csv('../input/cactus-images-csv/dataset/train.csv')
test = pd.read_csv('../input/cactus-images-csv/dataset/test.csv')
train = train.drop('Unnamed: 0', axis=1)
test = test.drop('Unnamed: 0', axis=1)
import keras
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, BatchNormalization, concatenate, AveragePooling2D
from keras.optimizers import Adam
def conv_layer(conv_x, filters):
conv_x = BatchNormalization()(conv_x)
conv_x = Activation('relu')(conv_x)
conv_x = Conv2D(filters, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(conv_x)
conv_x = Dropout(0.2)(conv_x)
return conv_x
def dense_block(block_x, filters, growth_rate, layers_in_block):
for i in range(layers_in_block):
each_layer = conv_layer(block_x, growth_rate)
block_x = concatenate([block_x, each_layer], axis=-1)
filters += growth_rate
return (block_x, filters)
def transition_block(trans_x, tran_filters):
trans_x = BatchNormalization()(trans_x)
trans_x = Activation('relu')(trans_x)
trans_x = Conv2D(tran_filters, (1, 1), kernel_initializer='he_uniform', padding='same', use_bias=False)(trans_x)
trans_x = AveragePooling2D((2, 2), strides=(2, 2))(trans_x)
return (trans_x, tran_filters)
def dense_net(filters, growth_rate, classes, dense_block_size, layers_in_block):
input_img = Input(shape=(32, 32, 3))
x = Conv2D(24, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(input_img)
dense_x = BatchNormalization()(x)
dense_x = Activation('relu')(x)
dense_x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(dense_x)
for block in range(dense_block_size - 1):
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x, filters = transition_block(dense_x, filters)
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x = BatchNormalization()(dense_x)
dense_x = Activation('relu')(dense_x)
dense_x = GlobalAveragePooling2D()(dense_x)
output = Dense(classes, activation='softmax')(dense_x)
return Model(input_img, output)
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
images = train.drop('label', axis=1)
images = np.asarray(images)
images = images.reshape(images.shape[0], 32, 32, 3)
label = train['label']
X_train, X_test, y_train, y_test = train_test_split(images, label.values, test_size=0.33)
Cat_test_y = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
dense_block_size = 3
layers_in_block = 4
growth_rate = 12
classes = 2
model = dense_net(growth_rate * 2, growth_rate, classes, dense_block_size, layers_in_block)
model.summary()
batch_size = 32
epochs = 10
optimizer = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=True, validation_data=(X_test, Cat_test_y)) | code |
16157191/cell_16 | [
"text_plain_output_1.png"
] | from keras.utils import np_utils
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
train = pd.read_csv('../input/cactus-images-csv/dataset/train.csv')
test = pd.read_csv('../input/cactus-images-csv/dataset/test.csv')
train = train.drop('Unnamed: 0', axis=1)
test = test.drop('Unnamed: 0', axis=1)
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
images = train.drop('label', axis=1)
images = np.asarray(images)
images = images.reshape(images.shape[0], 32, 32, 3)
label = train['label']
X_train, X_test, y_train, y_test = train_test_split(images, label.values, test_size=0.33)
Cat_test_y = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
print('X_train shape : ', X_train.shape)
print('y_train shape : ', y_train.shape)
print('X_test shape : ', X_test.shape)
print('y_test shape : ', y_test.shape) | code |
16157191/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/aerial-cactus-identification/train.csv')
df.head() | code |
16157191/cell_14 | [
"text_html_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, \
from keras.models import Model
import keras
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Activation, Dropout, GlobalAveragePooling2D, BatchNormalization, concatenate, AveragePooling2D
from keras.optimizers import Adam
def conv_layer(conv_x, filters):
conv_x = BatchNormalization()(conv_x)
conv_x = Activation('relu')(conv_x)
conv_x = Conv2D(filters, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(conv_x)
conv_x = Dropout(0.2)(conv_x)
return conv_x
def dense_block(block_x, filters, growth_rate, layers_in_block):
for i in range(layers_in_block):
each_layer = conv_layer(block_x, growth_rate)
block_x = concatenate([block_x, each_layer], axis=-1)
filters += growth_rate
return (block_x, filters)
def transition_block(trans_x, tran_filters):
trans_x = BatchNormalization()(trans_x)
trans_x = Activation('relu')(trans_x)
trans_x = Conv2D(tran_filters, (1, 1), kernel_initializer='he_uniform', padding='same', use_bias=False)(trans_x)
trans_x = AveragePooling2D((2, 2), strides=(2, 2))(trans_x)
return (trans_x, tran_filters)
def dense_net(filters, growth_rate, classes, dense_block_size, layers_in_block):
input_img = Input(shape=(32, 32, 3))
x = Conv2D(24, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False)(input_img)
dense_x = BatchNormalization()(x)
dense_x = Activation('relu')(x)
dense_x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(dense_x)
for block in range(dense_block_size - 1):
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x, filters = transition_block(dense_x, filters)
dense_x, filters = dense_block(dense_x, filters, growth_rate, layers_in_block)
dense_x = BatchNormalization()(dense_x)
dense_x = Activation('relu')(dense_x)
dense_x = GlobalAveragePooling2D()(dense_x)
output = Dense(classes, activation='softmax')(dense_x)
return Model(input_img, output) | code |
72077003/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type')['diff'].mean().plot(kind='bar') | code |
72077003/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False)
df_food = df.groupby('food_detail').agg({'id': 'count', 'diff': 'mean'}).sort_values(by='id', ascending=False).head(10).reset_index()
df_food.columns = ['food_detail', 'count', 'diff']
df_food | code |
72077003/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False)
df.groupby('food_type')['id'].count().plot(kind='bar') | code |
72077003/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False)
df[df['retailer_type'] == 'health food grocer']['food_type'].value_counts() | code |
72077003/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False)
df_food = df.groupby('food_detail').agg({'id': 'count', 'diff': 'mean'}).sort_values(by='id', ascending=False).head(10).reset_index()
df_r = df.groupby(['retailer_type', 'food_detail'])['diff'].mean().to_frame().reset_index()
df.groupby('organic')['diff'].sum()
df.groupby('organic')['diff'].mean() | code |
72077003/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df['diff'] = df['diff'].astype('str').apply(lambda x: x.split(' ')[0]).astype('int')
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False)
df_food = df.groupby('food_detail').agg({'id': 'count', 'diff': 'mean'}).sort_values(by='id', ascending=False).head(10).reset_index()
df_r = df.groupby(['retailer_type', 'food_detail'])['diff'].mean().to_frame().reset_index()
dup = df_r[df_r['food_detail'].duplicated()]
df_duplicates = df[df['food_detail'].apply(lambda x: x in dup['food_detail'].to_list())]
df_duplicates = df_duplicates.groupby(['retailer_type', 'food_detail']).agg({'diff': 'mean', 'id': 'count'})
df_duplicates.columns = ['diff', 'count']
df_duplicates.head() | code |
72077003/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df_labels.nlargest(5, 'count') | code |
72077003/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df_labels.nlargest(5, 'count')
df_labels.nlargest(5, 'count').plot.bar(y='diff') | code |
72077003/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False)
df_food = df.groupby('food_detail').agg({'id': 'count', 'diff': 'mean'}).sort_values(by='id', ascending=False).head(10).reset_index()
df_food.columns = ['food_detail', 'count', 'diff']
df_food.sort_values(by='diff').plot.bar(x='food_detail', y='diff') | code |
72077003/cell_46 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False)
df_food = df.groupby('food_detail').agg({'id': 'count', 'diff': 'mean'}).sort_values(by='id', ascending=False).head(10).reset_index()
df_r = df.groupby(['retailer_type', 'food_detail'])['diff'].mean().to_frame().reset_index()
df.groupby('organic')['diff'].sum() | code |
72077003/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False) | code |
72077003/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df = df[df['label_date'].notna()]
df = df[(df['diff'] > df['diff'].quantile(0.05)) & (df['diff'] < df['diff'].quantile(0.95))]
df_labels = df.groupby('label_language').agg({'id': 'count', 'diff': 'mean'})
df_labels.columns = ['count', 'diff']
df.groupby('retailer_type').count().id.to_frame().sort_values(by='id', ascending=False)
df[df['retailer_type'] == 'drugstore']['food_type'].value_counts() | code |
72077003/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/brooklyn-food-waste/brooklyn.csv')
df.head() | code |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.