path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73072707/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)... | code |
106192046/cell_42 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score
from sklearn.model_selection import K... | code |
106192046/cell_21 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import math
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
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
train_data = train_da... | code |
106192046/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
train_data = train_data.loc[:, train_data.isnull().sum() / len(train_data) * 100 < 60]
for a in train_data.columns:
if len(train_data[a].unique()) =... | code |
106192046/cell_25 | [
"text_html_output_1.png"
] | X_train.shape | code |
106192046/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.describe() | code |
106192046/cell_30 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
def getDatasetDetail(data):
return pd.DataFrame({'Datatype': data.dtypes.astype(str), 'Non_Null_Cou... | code |
106192046/cell_44 | [
"image_output_1.png"
] | from plot_metric.functions import BinaryClassification
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_cur... | code |
106192046/cell_20 | [
"text_plain_output_1.png"
] | import math
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
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
train_data = train_data.loc[:, train_data.isnull().sum() / len(train_data) * 100 < 6... | code |
106192046/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
def getDatasetDetail(data):
return pd.DataFrame({'Datatype': data.dtypes.astype(str), 'Non_Null_Count': data.count(axis=0).astype(int), 'Null_Count':... | code |
106192046/cell_39 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score
from sklearn.model_selection import K... | code |
106192046/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score
from sklearn.model_selection import K... | code |
106192046/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)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
def getDatasetDetail(data):
return pd.DataFrame({'Datatype': data.dtypes.astype(str), 'Non_Null_Count': data.count(axis=0).astype(int), 'Null_Count':... | code |
106192046/cell_50 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score
from sklearn.model_selection import K... | code |
106192046/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 |
106192046/cell_18 | [
"text_html_output_1.png"
] | import math
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
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
train_data = train_data.loc[:, train_data.isnull().sum() / len(train_data) * 100 < 6... | code |
106192046/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
def getDatasetDetail(data):
return pd.DataFrame({'Datatype': data.dtypes.astype(str), 'Non_Null_Cou... | code |
106192046/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
def getDatasetDetail(data):
return pd.DataFrame({'Datatype': data.dtypes.astype(str), 'Non_Null_Count': data.count(axis=0).astype(int), 'Null_Count':... | code |
106192046/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
train_data = train_data.loc[:, train_data.isnull().sum() / len(train_data) * 100 < 60]
train_data.drop(['PassengerId', 'Name'], axis=1, inplace=True)
d... | code |
106192046/cell_38 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score
from sklearn.model_selection import K... | code |
106192046/cell_47 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score
from sklearn.model_selection import K... | code |
106192046/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.head() | code |
106192046/cell_17 | [
"text_html_output_1.png"
] | import math
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
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
train_data = train_data.loc[:, train_data.isnull().sum() / len(train_data) * 100 < 6... | code |
106192046/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score
from sklearn.preprocessing import Sta... | code |
106192046/cell_43 | [
"text_html_output_1.png"
] | !pip install plot_metric | code |
106192046/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedShuffleSplit
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/t... | code |
106192046/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)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape
def getDatasetDetail(data):
return pd.DataFrame({'Datatype': data.dtypes.astype(str), 'Non_Null_Count': data.count(axis=0).astype(int), 'Null_Count':... | code |
106192046/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve
from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score
from sklearn.model_selection import K... | code |
106192046/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.shape | code |
106192046/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedShuffleSplit
from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
16115529/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import fastai
from fastai.train import Learner
from fastai.train import DataBunch
from fastai.callbacks import GeneralScheduler, TrainingPhase
from fastai.basic_data import DatasetType
import fastprogress
from fastprogress import force_console_behavior
import numpy as np
from pprint import pprint
import pandas as pd
im... | code |
16115529/cell_10 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.callbacks import GeneralScheduler, TrainingPhase
from fastprogress import force_console_behavior
from gensim.models import KeyedVectors
from keras.preprocessing import text, sequence
from scipy.stats import rankdata
from torch import nn
from torch.utils import data
from tqdm import tqdm
import copy
... | code |
332359/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
tra... | code |
332359/cell_2 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
tra... | code |
332359/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
train = pd.read_csv('../input/t... | code |
332359/cell_3 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
tra... | code |
332359/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from subprocess import check_output
train = pd.read_csv('../input/train... | code |
74063893/cell_42 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train... | code |
74063893/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
train_dataset['target'].value_counts() | code |
74063893/cell_25 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
print(stopwords.words('english')) | code |
74063893/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.DataFrame()
test_dataset_cleaned = pd.DataFrame(... | code |
74063893/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.DataFrame()
test_dataset_cleaned = pd.DataFrame(... | code |
74063893/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape | code |
74063893/cell_40 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
... | code |
74063893/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.DataFrame()
test_dataset_cleaned = pd.DataFrame(... | code |
74063893/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.DataFrame()
test_dataset_cleaned = pd.DataFrame(... | code |
74063893/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train... | code |
74063893/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
test_dataset.head() | code |
74063893/cell_45 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train... | code |
74063893/cell_49 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train... | code |
74063893/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.DataFrame()
test_dataset_cleaned = pd.DataFrame(... | code |
74063893/cell_32 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import pandas as pd
import re
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.... | code |
74063893/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.DataFrame()
test_dataset_cleaned = pd.DataFrame(... | code |
74063893/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.info() | code |
74063893/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
train_dataset['location'].value_counts() | code |
74063893/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
train_dataset['keyword'].value_counts() | code |
74063893/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
plt.f... | code |
74063893/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.DataFrame()
test_dataset_cleaned = pd.DataFrame(... | code |
74063893/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
train_dataset.head() | code |
74063893/cell_27 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import re
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.Da... | code |
74063893/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.head() | code |
74063893/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
train_dataset = pd.read_csv('../input/nlp-getting-started/train.csv')
test_dataset = pd.read_csv('../input/nlp-getting-started/test.csv')
train_dataset.shape
train_dataset.fillna('', inplace=True)
test_dataset.fillna('', inplace=True)
dataset = pd.DataFrame()
test_dataset_cleaned = pd.DataFrame(... | code |
74057429/cell_2 | [
"text_plain_output_1.png"
] | !pip install --upgrade tensorflow | code |
74057429/cell_5 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import tensorflow as tf
import tensorflow_datasets as tfds
def load_data():
mnist_train = tfds.load('mnist', split='train', shuffle_files=True)
x_train = np.zeros((60000, 28, 28, 1))
y_train = np.zeros((60000, 1))
i = 0
for ex in mnist_train:
x_train[i]... | code |
104118935/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
fig, ax = plt.subplots(figsize=(15,3))
ax=sns.countplot(x='launch site',data=df)
plt.xticks(rotation=90)
fig, ax = plt.subplots(figsiz... | code |
104118935/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
df['launch site'].groupby(df['launch site']).count().sort_values(ascending=False) | code |
104118935/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
def hlaunch_site(value):
a = str(value).split(' ')
if 'Satish' in a:
return 'Satish Dhawan Space Centre, Sriharikota, Andhra Pradesh'
else:
return value
df['launch... | code |
104118935/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df | code |
104118935/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
(df['launch site'] == 'Satish Dhawan Space Centre, Sriharikota, Andhra Pradesh').groupby(df['launch status']).count() | code |
104118935/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104118935/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
fig, ax = plt.subplots(figsize=(15,3))
ax=sns.countplot(x='launch site',data=df)
plt.xticks(rotation=90)
fig, ax = plt.subplots(figsiz... | code |
104118935/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
df['launch status'].groupby(df['launch status']).count() | code |
104118935/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
msno.bar(df, figsize=(6, 3), color='magenta') | code |
104118935/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
df[df['launch status'] == 0] | code |
104118935/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/list-of-indian-satellites/List_of_Indian_satellites.csv', encoding='cp1252')
df
fig, ax = plt.subplots(figsize=(15, 3))
ax = sns.countplot(x='launch site', data=df)
plt.xticks(rotation=90) | code |
324293/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=... | code |
324293/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), col... | code |
324293/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import colorsys
plt.style.use('seaborn-talk')
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', sep=',') | code |
324293/cell_18 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
import pandas as pd
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.va... | code |
324293/cell_15 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
import pandas as pd
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.va... | code |
324293/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
df.Age.hist(bins=100)
plt.xlabel('Age')
plt.title('Distribution of Age')
plt.show() | code |
324293/cell_12 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=... | code |
73079996/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow import keras
import pandas as pd
import tensorflow as tf
sample_submission = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
test = sample_submission
test['BraTS21ID5'] = [format(x, '05d') for x in test.BraTS21ID]
test_dataset = Dataset(test, is_trai... | code |
73079996/cell_9 | [
"image_output_1.png"
] | from pydicom.pixel_data_handlers.util import apply_voi_lut
import cv2
import glob
import matplotlib.pyplot as plt
import numpy as np
import pydicom
import re
data_directory = '../input/rsna-miccai-brain-tumor-radiogenomic-classification'
pytorch3dpath = '../input/efficientnetpyttorch3d/EfficientNet-PyTorch-3D'
m... | code |
73079996/cell_23 | [
"text_plain_output_1.png"
] | from pydicom.pixel_data_handlers.util import apply_voi_lut
from tensorflow import keras
import cv2
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pydicom
import re
import tensorflow as tf
data_directory = '../input/rsna-miccai-brain-tumor-radiogenomic-classification'... | code |
73079996/cell_19 | [
"image_output_1.png"
] | from tensorflow import keras
import pandas as pd
import tensorflow as tf
sample_submission = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
test = sample_submission
test['BraTS21ID5'] = [format(x, '05d') for x in test.BraTS21ID]
test_dataset = Dataset(test, is_trai... | code |
73079996/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
sample_submission = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/sample_submission.csv')
test = sample_submission
test['BraTS21ID5'] = [format(x, '05d') for x in test.BraTS21ID]
test.head(3) | code |
73079996/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from pydicom.pixel_data_handlers.util import apply_voi_lut
import cv2
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pydicom
import re
data_directory = '../input/rsna-miccai-brain-tumor-radiogenomic-classification'
pytorch3dpath = '../input/efficientnetpyttorch3d/Effic... | code |
73079996/cell_10 | [
"text_html_output_1.png"
] | from pydicom.pixel_data_handlers.util import apply_voi_lut
import cv2
import glob
import matplotlib.pyplot as plt
import numpy as np
import pydicom
import re
data_directory = '../input/rsna-miccai-brain-tumor-radiogenomic-classification'
pytorch3dpath = '../input/efficientnetpyttorch3d/EfficientNet-PyTorch-3D'
m... | code |
17139154/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
17139154/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1)
... | code |
17139154/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)
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.head(10) | code |
17139154/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
17139154/cell_33 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
17139154/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
17139154/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
17139154/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
17139154/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum=1)
pl... | code |
17139154/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
17139154/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
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