path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130008558/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_... | code |
130008558/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda ... | code |
130008558/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
130008558/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
... | code |
18127716/cell_25 | [
"text_html_output_1.png"
] | import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
import seaborn as sns
import seaborn as sns
sns.countplot(x='month', data=cal, hue='Reason', palette='viridis') | code |
18127716/cell_4 | [
"text_plain_output_1.png"
] | cal['twp'].value_counts().head(5) | code |
18127716/cell_23 | [
"text_plain_output_1.png"
] | import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
import seaborn as sns
sns.countplot(x='Day of Week', data=cal, hue='Reason', palette='viridis') | code |
18127716/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
import seaborn as sns
import seaborn as sns
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date()
cal['date'] = cal['timeStamp'... | code |
18127716/cell_20 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes | code |
18127716/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal['title'] | code |
18127716/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date()
cal['date'] = cal['timeStamp'].apply(lambda t: t.date())
cal.head() | code |
18127716/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
byMonth.head() | code |
18127716/cell_2 | [
"text_html_output_1.png"
] | cal.head(3) | code |
18127716/cell_11 | [
"text_plain_output_1.png"
] | cal.head(2) | code |
18127716/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
cal = pd.read_csv('../input/911.csv')
cal.info() | code |
18127716/cell_7 | [
"text_plain_output_1.png"
] | def ref_string(code):
if 'Fire' in code:
return 'Fire'
elif 'EMS' in code:
return 'EMS'
elif 'Traffic' in code:
return 'Traffic'
else:
return False
cal['Reason'] = cal['title'].apply(lambda x: ref_string(x))
cal['Reason'] | code |
18127716/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1) | code |
18127716/cell_32 | [
"text_html_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date()
cal['date'] = cal['timeStamp'].apply(lambda t: t.date())
x = cal.groupby('date').count().head()
hr = cal.groupby(by=['Day of Week', 'hour']).count()['Reason'].unstack()
print(hr) | code |
18127716/cell_28 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date() | code |
18127716/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal['Reason'].max() | code |
18127716/cell_15 | [
"text_plain_output_1.png"
] | cal.dtypes
time = cal['timeStamp'].iloc[0]
time.dayofweek | code |
18127716/cell_16 | [
"text_plain_output_1.png"
] | cal.dtypes
time = cal['timeStamp'].iloc[0]
time.dayofweek
cal['hour'] = cal['timeStamp'].apply(lambda time: time.hour)
cal['month'] = cal['timeStamp'].apply(lambda time: time.month)
cal['Day of Week'] = cal['timeStamp'].apply(lambda time: time.dayofweek)
cal['month']
cal['hour']
cal['Day of Week'] | code |
18127716/cell_3 | [
"text_plain_output_1.png"
] | cal['zip'].value_counts().head(5) | code |
18127716/cell_17 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.head(2) | code |
18127716/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date()
cal['date'] = cal['timeStamp'].apply(lambda t: t.date())
x = cal.groupby('date').count().head()
x['lat'].plot() | code |
18127716/cell_24 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
cal['month'] | code |
18127716/cell_14 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.info() | code |
18127716/cell_22 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
cal['Day of Week'] | code |
18127716/cell_10 | [
"text_html_output_1.png"
] | import seaborn as sns
import seaborn as sns
import seaborn as sns
sns.countplot(x='Reason', data=cal) | code |
18127716/cell_27 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
byMonth['lat'].plot() | code |
18127716/cell_12 | [
"text_plain_output_1.png"
] | cal.dtypes | code |
18127716/cell_5 | [
"text_html_output_1.png"
] | cal['title'].nunique() | code |
128005859/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv... | code |
128005859/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train,... | code |
128005859/cell_23 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClass... | code |
128005859/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
data | code |
128005859/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv... | code |
128005859/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
da... | code |
128005859/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape | code |
128005859/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
da... | code |
128005859/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, in... | code |
128005859/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifie... | code |
128005859/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
le = LabelEncoder()
data['Gender'] = le.fit_transform(data['Gender'])
data['Married'] = le.f... | code |
128005859/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train,... | code |
128005859/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape | code |
105177694/cell_2 | [
"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/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.head() | code |
105177694/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 |
105177694/cell_3 | [
"application_vnd.jupyter.stderr_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('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
import seaborn as sns
plt.figure(figsize=(10, 10))
sns.heatmap(data.isna()) | code |
104115351/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
from sklearn.multiclass import OutputCodeClassifier
from sklearn.svm import LinearSVC
from sklearn import datasets
from sklearn.multiclass import OutputCodeClassifier
from sklearn.svm import LinearSVC
X, y = datasets.load_iris(return_X_y=True)
clf = OutputCodeClassifier(LinearSVC(random_... | code |
104115351/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from skmultilearn.problem_transform import BinaryRelevance
from skmultilearn.problem_transform import BinaryRelevance
from sklearn.naive_bayes import GaussianNB
classifier = BinaryRelevance(GaussianNB())
from sklearn.mode... | code |
2010421/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe(include='all') | code |
2010421/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf | code |
2010421/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
train.head() | code |
2010421/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
2010421/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')
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
train['Name'] = train.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
test['Name'] = test.Name.str.extract(' ([A-Za-z]+... | code |
2010421/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe(include='all') | code |
89132121/cell_42 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
plt.figure(figsize=(10, 10))
sns.heatmap(RTA_Data.isnull(), cmap='binary') | code |
89132121/cell_57 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
RTA_Data.head(10) | code |
89132121/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | """for col in RTA_Data.columns:
if RTA_Data[col].dtype == 'object':
sns.countplot(y=col,data=RTA_Data)
plt.show()""" | code |
89132121/cell_65 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_73 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_54 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_67 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_60 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_69 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89132121/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
RTA_Data.info() | code |
89132121/cell_45 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_51 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_62 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_75 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_77 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_31 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_71 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
89132121/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum(... | code |
104127064/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2)
round(df.isna().sum(... | code |
104127064/cell_9 | [
"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)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df | code |
104127064/cell_33 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/t... | code |
104127064/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/t... | code |
104127064/cell_26 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104127064/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)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2) | code |
104127064/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104127064/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 |
104127064/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.