path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2011179/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any() | code |
2011179/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].s... | code |
2011179/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.head() | code |
74064945/cell_4 | [
"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/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.head() | code |
74064945/cell_8 | [
"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/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.quality.hist() | code |
74064945/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
fig = plt.figure(figsize=(5,5))
sns.barplot(x='quality', y='volatile acidity', data=df)
figure = plt.figur... | code |
74064945/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
fig = plt.figure(figsize=(5, 5))
sns.barplot(x='quality', y='volatile acidity', data=df) | code |
74064945/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)
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
fig = plt.figure(figsize=(5,5))
sns.barplot(x='quality', y='volatile acidity', data=df)
sns.barplot(x='qua... | code |
74064945/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.describe() | code |
106192728/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
import seaborn as sns
import matplotlib.pyplot as plt
plt.fi... | code |
106192728/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
df['disease'].value_counts() | code |
106192728/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
import seaborn as sns
import matplotlib.pyplot as plt
df.cor... | code |
106192728/cell_4 | [
"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/heart-disease-detection/heart_disease.csv')
df.columns | code |
106192728/cell_34 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x... | code |
106192728/cell_23 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
df.corr() | code |
106192728/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
from sklearn.mode... | code |
106192728/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
import seaborn as sns
import matplotlib.pyplot as plt
sns.co... | code |
106192728/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum() | code |
106192728/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
import seaborn as sns
import matplotlib.pyplot as plt
sns.co... | code |
106192728/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 |
106192728/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.info() | code |
106192728/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
import seaborn as sns
import matplotlib.pyplot as plt
sns.co... | code |
106192728/cell_32 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
from sklearn.mode... | code |
106192728/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T | code |
106192728/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
import seaborn as sns
import matplotlib.pyplot as plt
plt.fig... | code |
106192728/cell_3 | [
"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/heart-disease-detection/heart_disease.csv')
df.head() | code |
106192728/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
df['smoke'].value_counts() | code |
106192728/cell_35 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn a... | code |
106192728/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
from sklearn.mode... | code |
106192728/cell_14 | [
"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/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
df.head() | code |
106192728/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
import seaborn as sns
import matplotlib.pyplot as plt
plt.fi... | code |
106192728/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
import seaborn as sns
sns.countplot(x=df['disease']) | code |
106192728/cell_12 | [
"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/heart-disease-detection/heart_disease.csv')
df.columns
df.shape
df.isnull().sum()
df.describe().T
df.head() | code |
106192728/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/heart-disease-detection/heart_disease.csv')
df.columns
df.shape | code |
49129658/cell_21 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from keras.layers import Activation, Flatten, Dense, Dropout ,Input
from tensorflow.keras import backend
from tensorflow.keras import backend, models, layers, regularizers , optimizers
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.l... | code |
49129658/cell_20 | [
"image_output_1.png"
] | from keras.layers import Activation, Flatten, Dense, Dropout ,Input
from tensorflow.keras import backend
from tensorflow.keras import backend, models, layers, regularizers , optimizers
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import BatchNormalization , Concatenate
from ten... | code |
49129658/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_directory = '../input/100-bird-species/train'
val_directory = '../input/100-bird-species/valid'
test_directory = '../input/100-bird-species/test'
train_datagen = ImageDataGenerator(rescale=1 / 255)
val_datagen = ImageDataGenerator(rescale=1 / 2... | code |
49129658/cell_1 | [
"text_plain_output_100.png",
"text_plain_output_84.png",
"text_plain_output_56.png",
"text_plain_output_137.png",
"text_plain_output_139.png",
"text_plain_output_35.png",
"text_plain_output_130.png",
"text_plain_output_117.png",
"text_plain_output_98.png",
"text_plain_output_43.png",
"text_plain... | 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 |
49129658/cell_18 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from keras.layers import Activation, Flatten, Dense, Dropout ,Input
from tensorflow.keras import backend
from tensorflow.keras import backend, models, layers, regularizers , optimizers
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import BatchNormalization , Concatenate
from ten... | code |
49129658/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow.keras import backend
from tensorflow.keras import backend, models, layers, regularizers , optimizers
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import BatchNormalization , Concatenate
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_di... | code |
49129658/cell_14 | [
"image_output_1.png"
] | import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import os
import random
import numpy as np
import pandas as pd
import os
train_directory = '../input/100-bird-species/train'
val_directory = '../input/100-bird-species/valid'
test_directory = '../input/100-bird-species/test'
def display_r... | code |
49129658/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_directory = '../input/100-bird-species/train'
val_directory = '../input/100-bird-species/valid'
test_directory = '../input/100-bird-species/test'
train_datagen = ImageDataGenerator(rescale=1 / 255)
val_datagen = ImageDataGenerator(rescale=1 / 2... | code |
49129658/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_directory = '../input/100-bird-species/train'
val_directory = '../input/100-bird-species/valid'
test_directory = '../input/100-bird-species/test'
train_datagen = ImageDataGenerator(rescale=1 / 255)
val_datagen = ImageDataGenerator(rescale=1 / 2... | code |
2034195/cell_9 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
msno.bar(state_ts, color='r') | code |
2034195/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
data = [go.Scatter(x=state_ts['Date'], y=sta... | code |
2034195/cell_4 | [
"image_output_1.png"
] | import pandas as pd
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
print('Number of rows and columns in state ts:', state_ts.shape) | code |
2034195/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
data = [go.Scatter(x=state_ts['Date'], y=sta... | code |
2034195/cell_30 | [
"image_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
data = [go.Scatter(x=state_ts['Date'], y=sta... | code |
2034195/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
data = [go.Scatter(x=state_ts['Date'], y=sta... | code |
2034195/cell_6 | [
"image_output_1.png"
] | import pandas as pd
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
state_ts.info() | code |
2034195/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
plt.style.use('fivethirtyeight') | code |
2034195/cell_19 | [
"image_output_1.png"
] | import pandas as pd
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
state_vise = state_ts.groupby(['RegionName']).median()
state_vise.shape
state_month = state_ts.resample(... | code |
2034195/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
state_ts.describe() | code |
2034195/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
data = [go.Scatter(x=state_ts['Date'], y=sta... | code |
2034195/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
state_vise = state_ts.groupby(['RegionName']).median()
state_vise.shape | code |
2034195/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
cnt = state_ts['RegionName'].value_counts().to_fr... | code |
2034195/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
cnt = state_ts['RegionName'].value_counts().to_fr... | code |
2034195/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
cnt = state_ts['RegionName'].value_counts().to_fr... | code |
2034195/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
cnt = state_ts['RegionName'].value_counts().to_fr... | code |
2034195/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
missing = state_ts.isnull().sum().sum()
missing * 100 / (state_ts.shape[0] * state_ts.shape[1])
print('Date range:{} to {}'.format(state_ts['Date'].min(), state_ts['Date'].max()))
print('\n', state_ts[... | code |
2034195/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/'
state_ts = pd.read_csv(path + 'State_time_series.csv', parse_dates=['Date'])
state_ts.head() | code |
32068113/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.shape[0]
co... | code |
32068113/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.shape[0]
co... | code |
32068113/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.shape[0]
co... | code |
32068113/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.colum... | code |
32068113/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.shape[0]
co... | code |
32068113/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.shape[0]
co... | code |
32068113/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)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.shape[0] | code |
32068113/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.shape[0]
co... | code |
32068113/cell_3 | [
"text_plain_output_1.png",
"image_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 |
32068113/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.shape[0]
co... | code |
32068113/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
collision = pd.read_csv('../input/traffic-collision-data-from-2010-to-present/traffic-collision-data-from-2010-to-present.csv')
cols = collision.columns
cols = cols.map(lambda x: x.replace(' ', '_'))
collision.columns = cols
collision.head() | code |
32071330/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import numpy as np
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)
def plotPerColumnDi... | code |
32071330/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import numpy as np
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)
def plotPerColumnDi... | code |
32071330/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_6 | [
"image_output_1.png"
] | !pip install pycountry_convert
!pip install folium
!pip install plotly | code |
32071330/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_37 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt # plotting
import numpy as np
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)
def plotPerColumnDi... | code |
32071330/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
... | code |
32071330/cell_5 | [
"image_output_1.png"
] | import os # accessing directory structure
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73097119/cell_30 | [
"text_plain_output_1.png"
] | from os import listdir
from os.path import isfile, join
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Flatten
from tensorflow.kera... | code |
73097119/cell_29 | [
"image_output_11.png",
"image_output_98.png",
"image_output_74.png",
"image_output_82.png",
"image_output_24.png",
"image_output_46.png",
"image_output_85.png",
"image_output_25.png",
"image_output_77.png",
"image_output_47.png",
"image_output_78.png",
"image_output_17.png",
"image_output_30... | from os import listdir
from os.path import isfile, join
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Flatten
from tensorflow.kera... | code |
121151245/cell_13 | [
"text_plain_output_1.png"
] | code | |
121151245/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
path_validation = Path('/kaggle/input/food-validation/images/')
path_training = Path('/kaggle/input/food-training/images/')
image_files = os.listdir(path_training)
for i in range(2):
image_path = os.path.join(path_training, image_files[i])
... | code |
121151245/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
import os
import os
import numpy as np
import pandas as pd
import os
path_validation = Path('/kaggle/input/food-validation/images/')
path_training = Path('/kaggle/input/food-training/images/')
image_files = os.listdir(path_training)
for i in range(2):
image_path = os.path.join(path_trainin... | code |
121151245/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | !ls /kaggle/input/food-training | code |
121151245/cell_10 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
path_validation = Path('/kaggle/input/food-validation/images/')
path_training = Path('/kaggle/input/food-training/images/')
image_files = os.listdir(path_training)
for i in range(2):
image_path = os.path.join(path_training, image_files[i])
... | code |
18108547/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
18108547/cell_34 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
18108547/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
18108547/cell_33 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
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