path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122260046/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.cs... | code |
122260046/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 |
122260046/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
test.info() | code |
122260046/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import to_categorical
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
X = train.drop('label'... | code |
122260046/cell_28 | [
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # ... | code |
122260046/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
X = train.drop('label', axis=1).values / 255.0
X = X.reshape(-1, 28, 28, ... | code |
122260046/cell_16 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
A = test.values / 255.0
A = A.reshape(-1, 28, 28, 1)
A.shape | code |
122260046/cell_31 | [
"text_html_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # ... | code |
122260046/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
X = train.drop('label', axis=1).values / 255.0
X = X.reshape(-1, 28, 28, ... | code |
122260046/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from keras.models import Sequential
from tensorflow.keras.utils import plot_model
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormali... | code |
74070988/cell_11 | [
"text_html_output_1.png"
] | from finta import TA
from plotly.subplots import make_subplots
import glob
import numpy as np
import os
import pandas as pd
import plotly.graph_objects as go
import warnings
import os
import glob
import gc
import yaml
import math
import warnings
from tqdm import tqdm
from functools import reduce
import pandas a... | code |
74070988/cell_15 | [
"text_html_output_2.png"
] | from finta import TA
from joblib import Parallel, delayed
from plotly.subplots import make_subplots
from tqdm import tqdm
import gc
import glob
import numpy as np
import os
import pandas as pd
import plotly.graph_objects as go
import warnings
import os
import glob
import gc
import yaml
import math
import war... | code |
74070988/cell_10 | [
"text_plain_output_1.png"
] | from finta import TA
import glob
import numpy as np
import os
import pandas as pd
import warnings
import os
import glob
import gc
import yaml
import math
import warnings
from tqdm import tqdm
from functools import reduce
import pandas as pd
from finta import TA
from numba import jit
import numpy as np
from plotly... | code |
74070988/cell_5 | [
"text_plain_output_1.png"
] | !pip install finta --no-index --find-links=file:///kaggle/input/fin-ta/finta | code |
88086228/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', hue='Sex', data=train) | code |
88086228/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.shape | code |
88086228/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.head(2) | code |
88086228/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.boxplot(x='Survived', y='Fare', data=train) | code |
88086228/cell_57 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
def train_age(cols):
Age = cols[0]
Pclass = cols[... | code |
88086228/cell_56 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
def train_age(cols):
Age = cols[0]
Pclass = cols[... | code |
88086228/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
s... | code |
88086228/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', hue='SibSp', data=train)
plt.legend(loc='upper right', title='SibSp') | code |
88086228/cell_30 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
print('TRAIN DATASET')
print(train.isnull().sum() / len(train) * 100)
print('=' * 40)
print('TEST DATASET')
print(test.isnull().sum() / len(t... | code |
88086228/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', data=train) | code |
88086228/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape | code |
88086228/cell_40 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
print(train['Embarked'].value_counts()) | code |
88086228/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.boxplot(x='Survived', y='Age', data=train) | code |
88086228/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
train[train['Fare'] > 300] | code |
88086228/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.info() | code |
88086228/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
s... | code |
88086228/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.describe().transpose() | code |
88086228/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.shape
test.describe().transpose() | code |
88086228/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.shape
test.head(2) | code |
88086228/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique() | code |
88086228/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
s... | code |
88086228/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
test['Fare'].median() | code |
88086228/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
print('TRAIN DATASET')
print(train.isnull().sum() / len(t... | code |
88086228/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', hue='Parch', data=train) | code |
88086228/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.shape
test.info() | code |
88086228/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', hue='Pclass', data=train) | code |
88086228/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique() | code |
1006755/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
def find_one(substrs, superstr):
for substr in substrs:
if su... | code |
1006755/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes | code |
1006755/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape | code |
1006755/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes | code |
1006755/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
df[df['ADDRESS'] == '2912 W ROOSEVELT'] | code |
1006755/cell_44 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
def find_one(substrs, superstr):
for... | code |
1006755/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.info() | code |
1006755/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns | code |
1006755/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
def find_one(substrs, superstr):
for substr in substrs:
if su... | code |
1006755/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values | code |
1006755/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.describe(include='all') | code |
1006755/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
df[5:15] | code |
1006755/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape | code |
1006755/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.head() | code |
1006755/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.tail() | code |
1006755/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
def find_one(substrs, superstr):
for substr in substrs:
if su... | code |
1006755/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df[5:15] | code |
1006755/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
df.info() | code |
1006755/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df[df['LATITUDE'] == -1] | code |
1006755/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count() | code |
1006755/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
df['ADDRESS'].unique() | code |
73095876/cell_6 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
bigquery_client = bigquery.Client(project='wtm-kampala-ds', location='US')
dataset_ref = bigquery_client.dataset('openaq', project='bigquery-public-data')
dataset = bigquery_client.get_dataset(dataset_ref)
[x.table_id for x in bigquery_client.list_tables(dataset)] | code |
72098873/cell_21 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn a... | code |
72098873/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.plot(kind='scatter', x='SepalLengthCm', y='SepalWidthCm') | code |
72098873/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn import svm
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(i... | code |
72098873/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.info() | code |
72098873/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalL... | code |
72098873/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.head() | code |
72098873/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalL... | code |
72098873/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalL... | code |
72098873/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris['Species'].value_counts() | code |
72098873/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.rea... | code |
72098873/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iri... | code |
72098873/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend() | code |
128045865/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import pickle
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kag... | code |
128045865/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
import tensorflow as tf
import tensorflow.keras.backend a... | code |
128045865/cell_17 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kaggle/input/breas... | code |
128045865/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kaggle/input/breas... | code |
128045865/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import pandas as pd
breast_img = glob.glob('/kaggle/input/breast-histopathology-images/IDC_regular_ps50_idx5/**/*.png', recursive=True)
data = pd.read_csv('/kaggle/input/selected-imag... | code |
2017383/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.info() | code |
2017383/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
2017383/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import feature_selection
from sklearn import model_selection
from sklearn import tree
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data ... | code |
2017383/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
2017383/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import model_selection
from sklearn import tree
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Emb... | code |
2017383/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import feature_selection
from sklearn import model_selection
from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
t... | code |
2017383/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
33096987/cell_42 | [
"image_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
import seaborn as sns
p... | code |
33096987/cell_21 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sor... | code |
33096987/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)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df.info() | code |
33096987/cell_34 | [
"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)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def... | code |
33096987/cell_23 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean()... | code |
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