path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105176386/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
def cantidad_goles(texto):
ret... | code |
105176386/cell_19 | [
"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)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
m... | code |
105176386/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 |
105176386/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)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
cups | code |
105176386/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)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
players[players['Player Name'].str... | code |
105176386/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
matches = matches.dropna()
partid... | code |
105176386/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
matches = matches.dropna()
partid... | code |
105176386/cell_35 | [
"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
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world... | code |
105176386/cell_31 | [
"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
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world... | code |
105176386/cell_24 | [
"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
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world... | code |
105176386/cell_37 | [
"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
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world... | code |
105176386/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)
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCups.csv')
matches | code |
105176386/cell_36 | [
"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
players = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupPlayers.csv')
matches = pd.read_csv('/kaggle/input/fifa-world-cup/WorldCupMatches.csv')
cups = pd.read_csv('/kaggle/input/fifa-world... | code |
88090632/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df10B = pd.read_excel('../input/trabajohoras2/TrabajoHoras.xlsx')
df10B.loc[df10B['Nombres'] == 'Victoria'] | code |
2000591/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape | code |
2000591/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10, 10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True, annot=True)
sns.plt.title('Correlation Matrix Heatmap') | code |
2000591/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.head() | code |
2000591/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.describe() | code |
2000591/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib as matplot
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassi... | code |
2000591/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes | code |
2000591/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mydataset = pd.read_csv('HR_comma_sep.csv')
mydataset.shape
mydataset.dtypes
fig = plt.figure(figsize=(10,10))
corr = mydataset.corr()
sns.heatmap(corr, vmax=1, square=True,annot=True)
sns.plt.title('Correlation Matrix Heatmap')
mydataset.... | code |
2000591/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
mydataset = pd.read_csv('HR_comma_sep.csv') | code |
128011233/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges | code |
128011233/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
G = nx.Graph()
G.add_edges_from(edges.values)
node_degrees = {}
for no... | code |
128011233/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
nodes['education'].value_counts() | code |
128011233/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
G = nx.Graph()
G.add_edges_from(edges.values)
node_degrees = {}
for no... | code |
128011233/cell_28 | [
"text_html_output_1.png"
] | import networkx as nx
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
G = nx.Graph()
G.add_edges_from(edges.values)
radius = nx.radius(G)
diameter = nx.diameter(G)
print('Radius: ', radius)
pr... | code |
128011233/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes | code |
128011233/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
nodes = pd.read_csv('../input/tolokers/nodes.tsv', sep='\t', index_col='id')
nodes
edges = pd.read_csv('../input/tolokers/edges.tsv', sep='\t')
edges
G = nx.Graph()
G.add_edges_from(edges.values)
node_degrees = {}
for no... | code |
121149218/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum() | code |
121149218/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.head(5) | code |
121149218/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape | code |
121149218/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv.describe() | code |
121149218/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv['type'].value_counts().plot(kind='pie', autopct='%.2f') | code |
121149218/cell_33 | [
"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)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv['duration'] = mv['duration'].str.replace(' min', '')
mv['country'].value_counts().head(10... | code |
121149218/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv['duration'] = mv['duration'].str.replace(' min', '')
mv.head(6) | code |
121149218/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv['duration'] = mv['duration'].str.replace(' min', '')
mv.groupby('listed_in')['title'].count().sort_values(ascending=False)... | code |
121149218/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv['duration'] = mv['duration'].str.replace(' min', '')
mv.groupby('listed_in')['title'].count().sort_values(ascending=False)... | code |
121149218/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv | code |
121149218/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes | code |
121149218/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv | code |
121149218/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.tail(5) | code |
121149218/cell_3 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121149218/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3) | code |
121149218/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv['duration'] = mv['duration'].str.replace(' min', '')
mv.groupby('listed_in')['title'].count().sort_values(ascending=False)... | code |
121149218/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv['duration'] = mv['duration'].str.replace(' min', '')
a = mv['director'].value_counts().reset_index()[1:11]
a | code |
121149218/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
pd.crosstab(mv['country'], 'counts') | code |
121149218/cell_37 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mv = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
mv.shape
mv.sample(3)
mv.dtypes
mv.isnull().sum()
mv['duration'] = mv['duration'].str.replace(' min', '')
mv.groupby('listed_in')['title'].count().sort_values(ascending=False)... | code |
17123294/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
missing_data = train_df.isnull().sum()
percent_missing = round(missing_data / train_df.isnull().count() * 100, 2)
missing_df = pd.concat([missing_da... | code |
17123294/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
train_df.info() | code |
17123294/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set(style='darkgrid')
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
missing_data = train_df.isnull().sum()
percent_missing = round(mi... | code |
17123294/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set(style='darkgrid')
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
missing_data = train_df.isnull().sum()
percent_missing = round(mi... | code |
17123294/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set(style='darkgrid')
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
missing_data = train_df.isnull().sum()
percent_missing = round(mi... | code |
17123294/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set(style='darkgrid')
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
missing_data = train_df.isnull().sum()
percent_missing = round(mi... | code |
17123294/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
train_df.head() | code |
17123294/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set(style='darkgrid')
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
missing_data = train_df.isnull().sum()
percent_missing = round(mi... | code |
17123294/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
train_df.describe() | code |
17123294/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import numpy
import pandas
import seaborn
import sklearn
import sys
import sys
print('Python: {}'.format(sys.version))
import numpy
print('numpy: {}'.format(numpy.__version__))
import pandas
print('pandas: {}'.format(pandas.__version__))
import matplotlib
print('matplotlib: {}'.format(matplotlib... | code |
17123294/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
missing_data = train_df.isnull().sum()
percent_missing = round(missing_data / train_df.isnull().count() * 100, 2)
missing_df = pd.concat([missing_da... | code |
17123294/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape | code |
17123294/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set(style='darkgrid')
train_path = '../input/train.csv'
test_path = '../input/test.csv'
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.shape
missing_data = train_df.isnull().sum()
percent_missing = round(mi... | code |
1003448/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
print('Skewness: %f' % train['SalePrice'].skew())
print('Kurtosis: %f' % train['SalePrice'].kurt()) | code |
1003448/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
all_data = all_data.replace({'Utilities': {'AllPub': 1, 'NoSeWa': 0, 'NoSewr': 0, ... | code |
1003448/cell_20 | [
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1003448/cell_40 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('../inp... | code |
1003448/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | model.loc[30:, ['test-rmse-mean', 'train-rmse-mean']].plot() | code |
1003448/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClas... | code |
1003448/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClas... | code |
1003448/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe() | code |
1003448/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1003448/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat... | code |
1003448/cell_15 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
sns.distplot(train['SalePrice']) | code |
1003448/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
1003448/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1003448/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = p... | code |
1003448/cell_14 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='... | code |
1003448/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1003448/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train.head() | code |
1003448/cell_37 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = p... | code |
1003448/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe() | code |
1003448/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = p... | code |
2033577/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
train_df.head(5) | code |
2033577/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import keras
from keras.layers import *
from keras.models import *
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2033577/cell_7 | [
"text_plain_output_1.png"
] | import keras
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('../input/fashion-mnist_train.csv')
batch_size = 128
num_classes = 10
epochs = 10
img_rows, img_cols = (28, 28)
y_train = keras.utils.to_categorical(train_df.label.values, n... | code |
2033577/cell_8 | [
"text_plain_output_1.png"
] | import keras
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)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
batch_size = 128
num_classes = 10
epochs = 10
img_rows, img_cols = (28, 28)
y_train = keras.utils.to_cat... | code |
2033577/cell_3 | [
"text_plain_output_1.png"
] | import keras
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('../input/fashion-mnist_train.csv')
batch_size = 128
num_classes = 10
epochs = 10
img_rows, img_cols = (28, 28)
y_train = keras.utils.to_categorical(train_df.label.values, n... | code |
2033577/cell_5 | [
"text_plain_output_1.png"
] | import keras
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('../input/fashion-mnist_train.csv')
batch_size = 128
num_classes = 10
epochs = 10
img_rows, img_cols = (28, 28)
y_train = keras.utils.to_categorical(train_df.label.values, n... | code |
121152202/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df = pd.concat([df, df_add])
... | code |
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