path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130012258/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df | code |
130012258/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)
import re
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
import ... | code |
130012258/cell_27 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', '... | code |
130012258/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
df = df[df['Conten... | code |
122264951/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/nbagameinfo/nba_games (1).csv', index_col=0)
df
df = df.sort_values('date')
df
df = df.reset_index(drop=True)
df.drop(['mp.1', 'mp_opp.1', 'index_opp'], axis=1, inplace=True)
def add_target(team):
team['target'] = team['won'].shift(-1)
... | code |
122264951/cell_6 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | pip install scikit-learn | code |
122264951/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/nbagameinfo/nba_games (1).csv', index_col=0)
df
df = df.sort_values('date')
df | code |
122264951/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/nbagameinfo/nba_games (1).csv', index_col=0)
df | code |
122264951/cell_5 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/nbagameinfo/nba_games (1).csv', index_col=0)
df
df = df.sort_values('date')
df
df = df.reset_index(drop=True)
df.drop(['mp.1', 'mp_opp.1', 'index_opp'], axis=1, inplace=True)
def add_target(team):
team['target'] = team['won'].shift(-1)
... | code |
105207802/cell_63 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score,plot_confusion_matrix
from sklearn.svm import SVC
svc = SVC()
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
train_acc = round(svc.score(X_train, y_train) * 100, 1)
val_acc = round(accuracy_score(y_pred, y_test) * 100, 2)
plot_confusion_matrix(svc, X_test, y_test) | code |
105207802/cell_25 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[... | code |
105207802/cell_57 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pas... | code |
105207802/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pas... | code |
105207802/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
x = df['Type'].value_counts().plot.pie(explode=[0.5, 0.5, 0.5], autopct='%1.1f%%') | code |
105207802/cell_30 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No ... | code |
105207802/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No ... | code |
105207802/cell_74 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pas... | code |
105207802/cell_76 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pas... | code |
105207802/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pas... | code |
105207802/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df.columns
df.columns | code |
105207802/cell_61 | [
"text_html_output_1.png"
] | X_train | code |
105207802/cell_11 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df.head() | code |
105207802/cell_69 | [
"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 pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[... | code |
105207802/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, plot_confusion_matrix
import os
for dirname,... | code |
105207802/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pas... | code |
105207802/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No ... | code |
105207802/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No ... | code |
105207802/cell_62 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,plot_confusion_matrix
from sklearn.svm import SVC
svc = SVC()
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
train_acc = round(svc.score(X_train, y_train) * 100, 1)
val_acc = round(accuracy_score(y_pred, y_test) * 100, 2)
print('Training Accuracy :', train_acc, '%... | code |
105207802/cell_59 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pas... | code |
105207802/cell_58 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pas... | code |
105207802/cell_78 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score,plot_confusion_matrix
from sklearn.svm import SVC
svc = SVC()
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
train_acc = round(svc.score(X_train, y_train) * 100, 1)
val_acc = round(accuracy_score(y_pred, y_test) * 100, 2)
svc = SVC()
svc.fit(X_train2, y_train2)
y_pr... | code |
105207802/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No ... | code |
105207802/cell_75 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pas... | code |
105207802/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pas... | code |
105207802/cell_66 | [
"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 pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df.columns
df.columns
df | code |
105207802/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df.columns | code |
105207802/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pas... | code |
105207802/cell_14 | [
"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 seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No ... | code |
105207802/cell_27 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[... | code |
105207802/cell_37 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pas... | code |
105207802/cell_71 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pas... | code |
105207802/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
x = df['Target'].value_counts().plot.pie(explode=[0.5, 0.5], autopct='%1.1f%%') | code |
17112996/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
print(ipldf.columns)
print(ipldf.values) | code |
17112996/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
print(ipldf.head(5)) | code |
17112996/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17112996/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
idf = pd.DataFrame(ipldf.groupby('toss_winner').size())
print(idf.plot(kind='bar')) | code |
17112996/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
print(ipldf.describe()) | code |
17112996/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
idf = pd.DataFrame(ipldf.groupby('toss_winner').size())
idf2 = ipldf.groupby('winner').size()
print(idf2) | code |
17112996/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)
ipldf = pd.read_csv('../input/matches.csv')
print(ipldf.groupby('season').size())
print(ipldf.groupby('season').size().plot(kind='bar')) | code |
33112093/cell_25 | [
"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
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.corr()
simple = data1[['... | code |
33112093/cell_23 | [
"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
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.corr()
simple = data1[['... | code |
33112093/cell_20 | [
"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
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.corr() | code |
33112093/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)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter) | code |
33112093/cell_19 | [
"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
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.head() | code |
33112093/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 |
33112093/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('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum() | code |
33112093/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import *
lr = LinearRegression()
lr.fit(X_train, y_train)
print(lr.score(X_test, y_test)) | code |
33112093/cell_15 | [
"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
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
print(data.cut.unique())
print(data.color.uni... | code |
33112093/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.head(3) | code |
33112093/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import *
lr = LinearRegression()
lr.fit(X_train, y_train) | code |
33112093/cell_24 | [
"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
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.corr()
simple = data1[['... | code |
33112093/cell_27 | [
"text_plain_output_1.png"
] | print(X_train.shape)
print(X_test.shape) | code |
33112093/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
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
sns.heatmap(corr, xticklabels=corr.columns, yt... | code |
90142006/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 |
90142006/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn import preprocessing
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from skl... | code |
18102611/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
ser1... | code |
18102611/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data) | code |
18102611/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label) | code |
18102611/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
ser1... | code |
18102611/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d) | code |
18102611/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label) | code |
18102611/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
ser1... | code |
18102611/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
ser1... | code |
18102611/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label) | code |
104129687/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
df['track_i... | code |
104129687/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
df1 = pd.re... | code |
104129687/cell_1 | [
"text_plain_output_1.png"
] | import os
import plotly
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objs as go
import plotly
plotly.offline.init_notebook_mode(connected=True)
import warnings
warnings.filterwarnings('ignore')
import os
f... | code |
104129687/cell_7 | [
"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/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
df1 = pd.re... | code |
104129687/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/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
df1 = pd.re... | code |
104129687/cell_5 | [
"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/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'}) | code |
17118428/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/scmp2k19.csv')
df.info() | code |
17118428/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from bokeh.io import output_file,show,output_notebook,push_notebook
import os
import numpy as np
import pandas as pd
import seaborn as sns
from ipywidgets import interact
from bokeh.io import output_file, show, output_notebook, push_notebook
from bokeh.plotting import *
from bokeh.models import ColumnDataSource, Hove... | code |
18147081/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
categorical_feature = ['Name', 'Location', 'Fuel_Type', 'Transmission', 'Owner_Type']
train[categorical_feature].nunique() | code |
18147081/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape) | code |
18147081/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
test.sample(5)
list(train.Fuel_Type[~train.Fuel_Type.isin(test.Fuel_Type)].unique())
train = train[train.Fuel_Type != 'Electric']
... | code |
18147081/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report() | code |
18147081/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
def missing_values_table(df):
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mis_val_ta... | code |
18147081/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5) | code |
18147081/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
test.sample(5) | code |
18147081/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
test.sample(5)
list(train.Fuel_Type[~train.Fuel_Type.isin(test.Fuel_Type)].unique()) | code |
18147081/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
test.sample(5)
categorical_feature = ['Name', 'Location', 'Fuel_Type', 'Transmission', 'Owner_Type']
test[categorical_feature].nunique() | code |
18147081/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
test.sample(5)
list(train.Fuel_Type[~train.Fuel_Type.isin(test.Fuel_Type)].unique())
train = train[train.Fuel_Type != 'Electric']
... | code |
18147081/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
test.sample(5)
def missing_values_table(df):
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mis_val_table = pd.concat([mis_val,... | code |
104124186/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
x = df.YearsExperience.values.reshape(-1, 1)
y = df.Salary.values.reshape(-1, 1)
regressor = LinearRegression()
regressor.fit(... | code |
104124186/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
compare = pd.DataFrame({'Actual': y_test.flatten(), '... | code |
104124186/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
plt.scatter(a, b)
plt.plot(c, d)
plt.gri... | code |
104124186/cell_6 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train, y_train) | code |
104124186/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data | code |
104124186/cell_11 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
import numpy as np
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
print('mean absolute error: ', metrics.mean_absolute_error(y_test, y_pred))
print('mean squared_error: ', metrics... | code |
104124186/cell_7 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred | code |
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