path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90153288/cell_20 | [
"text_html_output_1.png"
] | from sklearn import linear_model
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/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for ... | code |
90153288/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 |
90153288/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)
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
for col in df.columns:
print(f'{col}:', df.dtypes[col]) | code |
90153288/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as... | code |
90153288/cell_28 | [
"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/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popu... | code |
90153288/cell_16 | [
"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/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popu... | code |
90153288/cell_17 | [
"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/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popu... | code |
90153288/cell_10 | [
"text_html_output_1.png",
"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/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=[10, 5])
sns.barplot(x=df.music_genre, y=df.pop... | code |
90153288/cell_12 | [
"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/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.lmplot(x='speechiness', y='popularity', data=df, ci=None, sca... | code |
90153288/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/prediction-of-music-genre/music_genre.csv')
print(df.shape)
df.head() | code |
130009806/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_mark... | code |
130009806/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
import plotly.... | code |
130009806/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_mark... | code |
130009806/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
def convert_dollar_number(data):
if typ... | code |
130009806/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)
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
fm.head() | code |
130009806/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 |
130009806/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_mark... | code |
130009806/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_mark... | code |
130009806/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv')
ci.head() | code |
130009806/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
ci = pd.read_csv('../input/farmers-markets-in-the-united-states/wiki_county_info.csv')
fm = pd.read_csv('../input/farmers-markets-in-the-united-states/farmers_mark... | code |
1006479/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
df_store.head() | code |
1006479/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv') | code |
1006479/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.head() | code |
73075563/cell_13 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import matthews_corrcoef
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0)
model.fit(X_train, y_train)
print(matthews_corrcoef(y_test, model.predict(X_test)))
print(accuracy_score(y_test, model.predict(X_test... | code |
73075563/cell_9 | [
"text_plain_output_1.png"
] | import hypertools as hyp
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.groupby('DEATH_EVENT').size()
labels = data['DEATH_EVENT']
hyp.plot(data, '.', hue=labels, ... | code |
73075563/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.head() | code |
73075563/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.describe() | code |
73075563/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 |
73075563/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.groupby('DEATH_EVENT').size() | code |
73075563/cell_18 | [
"text_plain_output_1.png"
] | from keras import callbacks
from keras.layers import Dense, BatchNormalization, Dropout, LSTM
from keras.models import Sequential
from sklearn.metrics import accuracy_score
from sklearn.metrics import matthews_corrcoef
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifie... | code |
73075563/cell_8 | [
"text_plain_output_1.png"
] | import hypertools as hyp
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.groupby('DEATH_EVENT').size()
labels = data['DEATH_EVENT']
hyp.plot(data, '.', hue=labels, r... | code |
73075563/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import sklearn
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import hypertools as hyp
import seaborn as sns
import numpy as np
import torch
!pip install captum
from captum.attr... | code |
73075563/cell_17 | [
"text_html_output_1.png"
] | import torch
import torch
import torch.nn as nn
import torch
import torch.nn as nn
class HeartFailureSimpleNNModel(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(12, 9)
self.ReLU1 = nn.ReLU()
self.linear2 = nn.Linear(9, 9)
self.ReLU2 = nn.ReLU(... | code |
73075563/cell_14 | [
"text_plain_output_1.png"
] | from keras import callbacks
from keras.layers import Dense, BatchNormalization, Dropout, LSTM
from keras.models import Sequential
from sklearn.metrics import accuracy_score
from sklearn.metrics import matthews_corrcoef
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifie... | code |
73075563/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum()
data.groupby('DEATH_EVENT').size()
corr = data.corr()
sns.heatmap(corr) | code |
73075563/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)
data = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')
data.isnull().sum() | code |
105206902/cell_42 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
from sklearn.ensemble import RandomForestClassifier
rfcfl = RandomForestCla... | code |
105206902/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal... | code |
105206902/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.describe() | code |
105206902/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal... | code |
105206902/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftest.head() | code |
105206902/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
... | code |
105206902/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal... | code |
105206902/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
from sklearn.svm import SVC
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)
... | code |
105206902/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
print(dftrain.shape)
print(dftest.shape)
print(dftotal.shape) | code |
105206902/cell_40 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
from sklearn.linear_model import LogisticRegression
lrclf = LogisticRegress... | code |
105206902/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
... | code |
105206902/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal... | code |
105206902/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test... | code |
105206902/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal... | code |
105206902/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.info() | code |
105206902/cell_45 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
... | code |
105206902/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
... | code |
105206902/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum() | code |
105206902/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum()
dftotal.isnull().sum()
dftotal... | code |
105206902/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftrain.head() | code |
105206902/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
... | code |
105206902/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns | code |
105206902/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.reset_index(inplace=True)
dftotal.columns
dftotal.isnull().sum() | code |
105206902/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/tabular-playground-series-aug-2022/train.csv')
dftest = pd.read_csv('../input/tabular-playground-series-aug-2022/test.csv')
dftotal = dftrain.append(dftest)
dftotal.head() | code |
89132929/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/re... | code |
89132929/cell_6 | [
"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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/cell_11 | [
"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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import folium
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89132929/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/cell_10 | [
"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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
89132929/cell_5 | [
"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)
lista = pd.read_csv('/kaggle/input/airbnb-gz/listings_gz.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-gz/calendar_gz.csv')
reviews = pd.read_csv('/kaggle/input/airbnb-gz/reviews_gz.csv')
mapa = plt.imread('/ka... | code |
122249887/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
useless_cols = ['PassengerId', 'Name', 'Cabin']
df.drop(columns=useless_cols, inplace=True)
import seaborn as sns
corr_matrix = df.... | code |
122249887/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing impor... | code |
122249887/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import ... | code |
122249887/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
useless_cols = ['PassengerId', 'Name', 'Cabin']
df.drop(columns=useless_cols, inplace=True)
import seaborn as sns
corr_matrix = df.... | code |
130002260/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.l... | code |
130002260/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('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.l... | code |
130002260/cell_25 | [
"text_plain_output_1.png"
] | from pprint import pprint
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pprint
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'ite... | code |
130002260/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories']... | code |
130002260/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('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe | code |
130002260/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.l... | code |
130002260/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.l... | code |
130002260/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from pprint import pprint
import matplotlib.pyplot as plt
import pprint
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130002260/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('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.info() | code |
130002260/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum() | code |
130002260/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.l... | code |
130002260/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('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'item']
highest_calorie_value = df['calories'].max()
lowest_calorie_item = df.l... | code |
130002260/cell_24 | [
"text_plain_output_1.png"
] | from pprint import pprint
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pprint
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'ite... | code |
130002260/cell_27 | [
"text_plain_output_1.png"
] | from pprint import pprint
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pprint
df = pd.read_csv('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df.describe
df.isnull().sum()
highest_calorie_item = df.loc[df['calories'].idxmax(), 'ite... | code |
130002260/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('/kaggle/input/starbucks-nutrition/starbucks.csv', index_col=0)
df | code |
88100087/cell_23 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import KBinsDiscretizer
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
df.dropna(inplace=True)
X = df.iloc[:, 1:]
y = df.iloc[:, 0]
kb... | code |
88100087/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88100087/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
df.head() | code |
88100087/cell_17 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import KBinsDiscretizer
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
df.dropna(inplace=True)
X = df.iloc[:, 1:]
y = df.iloc[:, 0]
kbin_age = KBinsDiscretizer(n_bins=15, encode='... | code |
88100087/cell_14 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import KBinsDiscretizer
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
df.dropna(inplace=True)
X = df.iloc[:, 1:]
y = df.iloc[:, 0]
kbin_age = KBinsDiscretizer(n_bins=15, encode='... | code |
88100087/cell_12 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import KBinsDiscretizer
import pandas as pd
df = pd.read_csv('/kaggle/input/titanic/train.csv', usecols=['Age', 'Fare', 'Survived'])
kbin_age = KBinsDiscretizer(n_bins=15, encode='ordinal', strategy='quantile')
kbin_fare = KBinsDiscretizer(n_b... | code |
105194628/cell_2 | [
"text_plain_output_1.png"
] | input1 = int(input('Enter a integer'))
if input1 > 0:
print('The number is postive')
elif input1 < 0:
print('The number is not positive')
else:
print('The number is zero') | code |
105194628/cell_7 | [
"text_plain_output_1.png"
] | n1 = int(input('Enter your number 1'))
n2 = int(input('Enter your number 2'))
n3 = int(input('Enter your number 3'))
if n1 > n2 and n3:
print(n1, 'It is the maximun value')
elif n2 > n1 and n3:
print(n2, 'It is the maximun value')
elif n3 > n1 and n2:
print(n3, 'It is the maximun value') | code |
105194628/cell_8 | [
"text_plain_output_1.png"
] | n1 = int(input('Enter your number 1'))
n2 = int(input('Enter your number 2'))
n3 = int(input('Enter your number 3'))
max = n1
if max < n2:
max = n2
if max < n3:
max = n3
print(max) | code |
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