path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
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=['di... | 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=['di... | 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=['di... | 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='cent... | 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().unst... | 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
# Pri... | 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 ... | 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['... | 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'] < snReco... | 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... | 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 im... | 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... | 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... | 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-... | 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('/... | 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 = []... | 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 = []... | 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 =... | 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 correl... | 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','le... | 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 impo... | 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_... | 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 = [... | 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 = ... | 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_co... | 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_co... | 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()
h... | 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()
h... | 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().su... | 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()
h... | 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().su... | 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().su... | 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()
h... | 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()
h... | 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... | 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/CO... | 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-res... | 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... | 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/me... | 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",
"t... | !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'] =... | 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', 'Arrival... | 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', 'Arrival... | 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', 'Arrival... | 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', 'Arrival... | 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', 'Depature... | 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', 'Arrival... | 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, ... | 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
impo... | 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('labe... | 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, ... | 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_... | 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, ... | 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)
im... | 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)
im... | 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... | 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... | 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
... | 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.preprocessin... | 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 i... | 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... | 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_data... | 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 subpr... | 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... | code |
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