path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130015002/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
test_df.shape
test_df['Total Rooms'] = test_df['AveRooms'].apply(lambda x: int(x))
test_df = test_df.drop(['AveRooms'], axis=1)
test_df['HouseAge'] = tes... | code |
130015002/cell_29 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
test_df.shape | code |
130015002/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=1)
reg.fit(X_train, y_train)
pred = reg.predict(X_val)
from sklearn.metrics import mean_absolute_error
mae = mean_absolute_e... | code |
130015002/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df = train_df.drop(['AveRooms'], ... | code |
130015002/cell_41 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
test_df.shape
test_df['Total Rooms'] = test_df['AveRooms'].apply(lambda x: int(x))
test_df = test_df.... | code |
130015002/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum() | code |
130015002/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.head() | code |
130015002/cell_28 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
test_df.head() | code |
130015002/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape | code |
130015002/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df = train_df.drop(['AveRooms'], ... | code |
130015002/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df.describe() | code |
130015002/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df = train_df.drop(['AveRooms'], axis=1)
train_df = train_df.drop(['AveBedrms'], axis=1)... | code |
130015002/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=1)
reg.fit(X_train, y_train) | code |
50227915/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', low_memory=False)
data.columns = data.iloc[0]
data.drop(data.index[0], inplace=True)
questions = list(data.columns)
question_df = pd.DataFrame(data.columns, columns=['questions'])
questions | code |
50227915/cell_2 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', low_memory=False)
print(data.shape)
data.columns = data.iloc[0]
data.drop(data.index[0], inplace=True)
data.head() | code |
50227915/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
sns.set_style(style='whitegrid')
data = pd.read_csv('/kaggle/input/kaggl... | code |
50227915/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
sns.set_style(style='whitegrid')
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_respon... | code |
50227915/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
sns.set_style(style='whitegrid')
data = pd.read_csv('/kaggle/input/kaggl... | code |
50227915/cell_3 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', low_memory=False)
data.columns = data.iloc[0]
data.drop(data.index[0], inplace=True)
questions = list(data.columns)
question_df = pd.DataFrame(data.columns, columns=['questions'])
print(questions[:15]) | code |
50227915/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
sns.set_style(style='whitegrid')
data = pd.read_csv('/kaggle/input/kaggl... | code |
50227915/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
sns.set_style(style='whitegrid')
data = pd.read_csv('/kaggle/input/kaggl... | code |
50227915/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
sns.set_style(style='whitegrid')
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_respon... | code |
90155131/cell_13 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras import Model, Sequential
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Add, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, Input, Activation, Dense, Flatten
from tensorflow.keras.layers import Dense, Flatten, Conv2D, Concatenate, Add
from tensor... | code |
90155131/cell_9 | [
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras import Model, Sequential
from tensorflow.keras.layers import Add, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, Input, Activation, Dense, Flatten
from tensorflow.keras.layers import Dense, Flatten, Conv2D, Concatenate, Add
from tensorflow.keras.regularizers import l2
from tensorf... | code |
90155131/cell_18 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
def show_image(image):
plt.colorbar()
imagenum = np.random.randint(len(x_train))
classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def plot_metrics(metric_name, title, append='val_'):
plt.xticks(list(range(l... | code |
90155131/cell_16 | [
"image_output_1.png"
] | from tensorflow import GradientTape
from tensorflow.keras import Model, Sequential
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Add, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, Input, Activation, Dense, Flatten
from tensorflow.keras.layers import Dense, Flatten, ... | code |
90155131/cell_3 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_1... | from tensorflow.keras import Model, Sequential
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers.experimental.preprocessing import RandomFlip, RandomRotation, RandomZoom, RandomTranslation
import tensorflow as tf
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Co... | code |
90155131/cell_17 | [
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
def show_image(image):
plt.colorbar()
imagenum = np.random.randint(len(x_train))
classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def plot_metrics(metric_name, title, append='val_'):
plt.xticks(list(range(l... | code |
90155131/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
def show_image(image):
plt.colorbar()
imagenum = np.random.randint(len(x_train))
classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def plot_metrics(metric_name, title, append='val_'):
plt.xticks(list(range(l... | code |
90155131/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
def show_image(image):
plt.figure()
plt.imshow(image)
plt.colorbar()
plt.grid(False)
plt.show()
imagenum = np.random.randint(len(x_train))
show_image(x_train[imagenum].reshape(32, 32, 3))
classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',... | code |
90136679/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import spacy
f = open('/kaggle/input/harry-potter-sorcerers-stone/Harry-potter-sorcerers-stone.txt', 'r')
hp_book = ''
lines = []
for line in f:
stripped_line = line.rstrip() + ' '
hp_book += stripped_line
lines.append(line)
f.close()
nlp = spacy.load('en_core_web_lg')
doc = nlp(hp_book) | code |
128003024/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': '... | code |
128003024/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': 'max_heartrate', 'exang': 'exc_angina', 'ca': 'major_vess... | code |
128003024/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': '... | code |
128003024/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128003024/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': '... | code |
128003024/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': 'max_heartrate', 'exang'... | code |
128003024/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
df.head() | code |
128003024/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': '... | code |
128003024/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': 'max_heartrate', 'exang': 'exc_angina', 'ca': 'major_vess... | code |
128003024/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
df.info() | code |
128003024/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': 'max_heartrate', 'exang'... | code |
128003024/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df) | code |
128003024/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': 'max_heartrate', 'exang'... | code |
128003024/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (8, 5)
plt.style.use('fivethirtyeight')
import seaborn as sns
import plotly.express as p
from plotly.offline import iplot
import os
import glob
from sklearn.cluster import KMeans | code |
128003024/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': 'max_heartrate', 'exang'... | code |
128003024/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': '... | code |
128003024/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': 'max_heartrate', 'exang': 'exc_angina', 'ca': 'major_vess... | code |
128003024/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': '... | code |
128003024/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/hearts/heart.csv')
np.shape(df)
df.rename(columns={'cp': 'chest_pain_type', 'trestbps': 'bloodpress_med', 'chol': 'chol_serum', 'fbs': 'bloodsugar_fast', 'restecg': 'rest_ecg', 'thalach': 'max_heartrate', 'exang': 'exc_angina', 'ca': 'major_vess... | code |
90108118/cell_13 | [
"text_plain_output_1.png"
] | from absl import flags
from gezi import tqdm
import gezi
import glob
import os
from IPython.display import display
import tensorflow as tf
import torch
from absl import flags
FLAGS = flags.FLAGS
from transformers import AutoTokenizer
from datasets import Dataset
from src import config
from src.util import *
from s... | code |
90108118/cell_9 | [
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"text_html_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_out... | import gezi
import glob
import os
gezi.init_flags()
model_root = '../input'
model_dirs = [x for x in glob.glob(f'{model_root}/feedback-model*') if os.path.isdir(x)]
model_dirs = [f'../input/feedback-model{i}' for i in range(len(model_dirs))]
model_dir = model_dirs[0]
tf_models = []
first_models = []
ic(first_models... | code |
90108118/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install -q icecream --no-index --find-links=file:///kaggle/input/icecream/ | code |
90108118/cell_1 | [
"text_plain_output_1.png"
] | import sys
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import traceback
!ln -s ../input/feedback ./src
if os.path.exists('/kaggle'):
sys.path.append('/kaggle/input/pikachu/utils')
sys.path.append('/kaggle/input/pikachu/third')
sys.path.append('.')
!ls ../input | code |
90108118/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | num_test_ids = 1000
folds = pd.read_csv('../input/feedback/folds.csv')
test_ids = folds[folds.kfold == 0].id.values
test_ids.sort()
test_ids = test_ids[:num_test_ids]
len(test_ids) | code |
90108118/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | ic(P) | code |
90108118/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install -q pymp-pypi --no-index --find-links=file:///kaggle/input/pymp-pypi/pymp-pypi-0.4.5/dist | code |
90108118/cell_14 | [
"text_plain_output_1.png"
] | from absl import flags
from gezi import tqdm
import gezi
import glob
import os
from IPython.display import display
import tensorflow as tf
import torch
from absl import flags
FLAGS = flags.FLAGS
from transformers import AutoTokenizer
from datasets import Dataset
from src import config
from src.util import *
from s... | code |
90108118/cell_12 | [
"text_plain_output_1.png"
] | from absl import flags
from gezi import tqdm
import gezi
import glob
import os
from IPython.display import display
import tensorflow as tf
import torch
from absl import flags
FLAGS = flags.FLAGS
from transformers import AutoTokenizer
from datasets import Dataset
from src import config
from src.util import *
from s... | code |
32062709/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
32062709/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.head() | code |
32062709/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
32062709/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
32062709/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 |
32062709/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
32062709/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
32062709/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list() | code |
32062709/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
32062709/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
32062709/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
32062709/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape | code |
32062709/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
economics = pd.read_csv('/kaggle/input/the-economic-freedom-index/economic_freedom_index2019_data.csv', index_col='Country', encoding='ISO-8859-1')
economics.columns.to_list()
economics.shape
economics = economics[['Region', 'World Rank', 'Regio... | code |
106208845/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = lis... | code |
106208845/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
columns_contains_null = [co... | code |
106208845/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
df | code |
106208845/cell_20 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
columns... | code |
106208845/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
df.describe().T | code |
106208845/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = lis... | code |
106208845/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
print(f'Numerical columns: \n\n{num_cols}\n\nCategorical columns: \n\n{c... | code |
106208845/cell_18 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
columns... | code |
106208845/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
columns_contains_null = [col for col in df.columns if df[col].isnull().... | code |
106208845/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = lis... | code |
106208845/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
columns... | code |
106208845/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = lis... | code |
106208845/cell_14 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
columns... | code |
106208845/cell_22 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
columns... | code |
106208845/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
pd.set_option('display.max_columns', None)
df.describe().T
num_cols = list(df.select_dtypes(include=['int64', 'float64']).columns)
cat_cols = list(df.select_dtypes(include=['object']).columns)
columns_contains_null = [co... | code |
106208845/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa-world-cup-2022/international_matches.csv')
df.info() | code |
18159050/cell_9 | [
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Dense, Conv2D, Flatten
from keras.models import Sequential, Model
from keras.optimizers import Adagrad
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
train_datagen = Ima... | code |
18159050/cell_6 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Dense, Conv2D, Flatten
from keras.models import Sequential, Model
from keras.optimizers import Adagrad
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1.0 / 255... | code |
18159050/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator | code |
18159050/cell_7 | [
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Dense, Conv2D, Flatten
from keras.models import Sequential, Model
from keras.optimizers import Adagrad
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
train_datagen = Ima... | code |
18159050/cell_3 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1.0 / 255, horizontal_flip=True, vertical_flip=True)
def get_generator(path):
return train_datagen.flow_from_directory(path, target_size=(40, 96), batch_size=32, class_mode='categorical', color_mode='grayscale')
tra... | code |
72062410/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_drop_unrated = ramen_drop_unrated[ramen_drop_unrated['Stars'] != 'Unrated']
ramen_drop_unrated.groupby('... | code |
72062410/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen['Stars'].value_counts() | code |
72062410/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen[ramen['Country'] == 'Japan'] | code |
72062410/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_drop_unrated = ramen_drop_unrated[ramen_drop_unrated['Stars'] != 'Unrated']
ramen_drop_unrated.groupby('... | code |
72062410/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_convert_unrated.groupby('Style')['rating'].mean() | code |
72062410/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen.head() | code |
72062410/cell_2 | [
"text_plain_output_1.png"
] | farbe = 'grün'
print(farbe) | code |
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