path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
34147773/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
unique_example_uids = set(example_uids)
len(unique_example_uids)
embeddings_df = pd.read_csv('../input/CORD-19-research-challenge/cord_19_embeddings_4_24/cord_19_embeddings_4_24.csv', header=None, index_col=0)
available_uids = unique_example_uids.intersection(embeddings_df.index)
example_embeddings_df = embeddings_df.loc[available_uids]
feature_pop_means = embeddings_df.mean(0)
informative_embeddings_df = embeddings_df.loc[:, p_vals < 0.05 / len(p_vals)]
clustering = KMeans(n_clusters=10, random_state=0).fit(informative_embeddings_df.values)
labels = clustering.labels_
uid_cluster_map = dict(zip(informative_embeddings_df.index, labels))
for i in range(1, 11):
print(i)
cluster_ids = set([k for k, v in uid_cluster_map.items() if v == i])
cluster_ids = cluster_ids.intersection(metadata_df.index)
for ele in metadata_df.loc[cluster_ids, 'title']:
if isinstance(ele, str):
if ('corona' in ele.lower() or 'cov' in ele.lower()) and ('humid' in ele.lower() or 'temperature' in ele.lower()):
print('\t', ele) | code |
34147773/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
unique_example_uids = set(example_uids)
len(unique_example_uids)
embeddings_df = pd.read_csv('../input/CORD-19-research-challenge/cord_19_embeddings_4_24/cord_19_embeddings_4_24.csv', header=None, index_col=0)
available_uids = unique_example_uids.intersection(embeddings_df.index)
example_embeddings_df = embeddings_df.loc[available_uids]
example_embeddings_df | code |
34147773/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
unique_example_uids = set(example_uids)
len(unique_example_uids)
embeddings_df = pd.read_csv('../input/CORD-19-research-challenge/cord_19_embeddings_4_24/cord_19_embeddings_4_24.csv', header=None, index_col=0)
available_uids = unique_example_uids.intersection(embeddings_df.index)
example_embeddings_df = embeddings_df.loc[available_uids]
for i in range(1, 21, 2):
plt.scatter(embeddings_df[i], embeddings_df[i + 1])
plt.scatter(example_embeddings_df[i], example_embeddings_df[i + 1])
plt.show() | code |
34147773/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid') | code |
34147773/cell_40 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
unique_example_uids = set(example_uids)
len(unique_example_uids)
embeddings_df = pd.read_csv('../input/CORD-19-research-challenge/cord_19_embeddings_4_24/cord_19_embeddings_4_24.csv', header=None, index_col=0)
available_uids = unique_example_uids.intersection(embeddings_df.index)
example_embeddings_df = embeddings_df.loc[available_uids]
feature_pop_means = embeddings_df.mean(0)
informative_embeddings_df = embeddings_df.loc[:, p_vals < 0.05 / len(p_vals)]
clustering = KMeans(n_clusters=10, random_state=0).fit(informative_embeddings_df.values)
labels = clustering.labels_
uid_cluster_map = dict(zip(informative_embeddings_df.index, labels))
example_clusters = [uid_cluster_map[uid] for uid in example_embeddings_df.index]
example_clusters | code |
34147773/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
metadata_df | code |
34147773/cell_32 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
unique_example_uids = set(example_uids)
len(unique_example_uids)
embeddings_df = pd.read_csv('../input/CORD-19-research-challenge/cord_19_embeddings_4_24/cord_19_embeddings_4_24.csv', header=None, index_col=0)
available_uids = unique_example_uids.intersection(embeddings_df.index)
example_embeddings_df = embeddings_df.loc[available_uids]
feature_pop_means = embeddings_df.mean(0)
informative_embeddings_df = embeddings_df.loc[:, p_vals < 0.05 / len(p_vals)]
informative_embeddings_df | code |
34147773/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
unique_example_uids = set(example_uids)
len(unique_example_uids)
embeddings_df = pd.read_csv('../input/CORD-19-research-challenge/cord_19_embeddings_4_24/cord_19_embeddings_4_24.csv', header=None, index_col=0)
available_uids = unique_example_uids.intersection(embeddings_df.index)
example_embeddings_df = embeddings_df.loc[available_uids]
plt.bar(range(len(p_vals)), -np.log(p_vals))
plt.hlines(-np.log(0.05), 0, 800)
plt.hlines(-np.log(0.05 / len(p_vals)), 0, 800) | code |
34147773/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
example_uids | code |
34147773/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
unique_example_uids = set(example_uids)
len(unique_example_uids) | code |
34147773/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_df | code |
34147773/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import collections
import pandas as pd
metadata_df = pd.read_csv('../input/CORD-19-research-challenge/metadata.csv', index_col='cord_uid')
example_df = pd.read_csv('../input/CORD-19-research-challenge/Kaggle/target_tables/2_relevant_factors/How does temperature and humidity affect the transmission of 2019-nCoV.csv')
example_shas = []
example_uids = []
for index, row in example_df.iterrows():
study_title = row['Study']
study_metadata = metadata_df[metadata_df['title'] == study_title]
if len(study_metadata) != 0:
sha = study_metadata.iloc[0]['sha']
uid = study_metadata.iloc[0].name
if str(sha) != 'nan':
example_shas.append(sha)
example_uids.append(uid)
unique_example_uids = set(example_uids)
len(unique_example_uids)
embeddings_df = pd.read_csv('../input/CORD-19-research-challenge/cord_19_embeddings_4_24/cord_19_embeddings_4_24.csv', header=None, index_col=0)
available_uids = unique_example_uids.intersection(embeddings_df.index)
example_embeddings_df = embeddings_df.loc[available_uids]
feature_pop_means = embeddings_df.mean(0)
informative_embeddings_df = embeddings_df.loc[:, p_vals < 0.05 / len(p_vals)]
clustering = KMeans(n_clusters=10, random_state=0).fit(informative_embeddings_df.values)
labels = clustering.labels_
collections.Counter(labels) | code |
128023859/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
def create_ensamble_submission():
submission_prognosis_list = []
for i in range(0, len(results_gbc)):
votes = np.zeros(11)
sorted = np.argsort(results_gbc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
sorted = np.argsort(results_svc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
sorted = np.argsort(results_nnc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
response = classes[np.argmax(votes)]
votes[np.argmax(votes)] = 0
response += ' ' + classes[np.argmax(votes)]
votes[np.argmax(votes)] = 0
response += ' ' + classes[np.argmax(votes)]
submission_prognosis_list += [response]
submission_df = pd.DataFrame()
submission_df['id'] = test_df['id']
submission_df['prognosis'] = submission_prognosis_list
return submission_df
train_df.columns
X = train_df.to_numpy()[:, 1:-1]
X.shape
y = train_df['prognosis']
classes = sorted(list(train_df['prognosis'].unique()))
X.shape
validate_records = 4
clf_gbc = GradientBoostingClassifier(n_estimators=2500, learning_rate=0.001, max_features=30, max_depth=3, random_state=0, subsample=0.6).fit(X[validate_records:], y[validate_records:])
clf_svc = make_pipeline(StandardScaler(), SVC(gamma='auto', kernel='poly', probability=True))
clf_svc.fit(X, y)
clf_nnc = MLPClassifier(random_state=0, hidden_layer_sizes=3, max_iter=1000).fit(X[validate_records:], y[validate_records:])
results_gbc = clf_gbc.predict_proba(test_df.to_numpy()[:, 1:])
results_svc = clf_svc.predict_proba(test_df.to_numpy()[:, 1:])
results_nnc = clf_nnc.predict_proba(test_df.to_numpy()[:, 1:])
submission_df = create_ensamble_submission()
submission_df | code |
128023859/cell_9 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
def create_ensamble_submission():
submission_prognosis_list = []
for i in range(0, len(results_gbc)):
votes = np.zeros(11)
sorted = np.argsort(results_gbc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
sorted = np.argsort(results_svc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
sorted = np.argsort(results_nnc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
response = classes[np.argmax(votes)]
votes[np.argmax(votes)] = 0
response += ' ' + classes[np.argmax(votes)]
votes[np.argmax(votes)] = 0
response += ' ' + classes[np.argmax(votes)]
submission_prognosis_list += [response]
submission_df = pd.DataFrame()
submission_df['id'] = test_df['id']
submission_df['prognosis'] = submission_prognosis_list
return submission_df
train_df.columns
X = train_df.to_numpy()[:, 1:-1]
X.shape
y = train_df['prognosis']
classes = sorted(list(train_df['prognosis'].unique()))
X.shape
validate_records = 4
clf_gbc = GradientBoostingClassifier(n_estimators=2500, learning_rate=0.001, max_features=30, max_depth=3, random_state=0, subsample=0.6).fit(X[validate_records:], y[validate_records:])
if validate_records > 0:
print(clf_gbc.score(X[:validate_records], y[:validate_records])) | code |
128023859/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
train_df.columns | code |
128023859/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
train_df.columns
X = train_df.to_numpy()[:, 1:-1]
X.shape | code |
128023859/cell_11 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
def create_ensamble_submission():
submission_prognosis_list = []
for i in range(0, len(results_gbc)):
votes = np.zeros(11)
sorted = np.argsort(results_gbc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
sorted = np.argsort(results_svc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
sorted = np.argsort(results_nnc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
response = classes[np.argmax(votes)]
votes[np.argmax(votes)] = 0
response += ' ' + classes[np.argmax(votes)]
votes[np.argmax(votes)] = 0
response += ' ' + classes[np.argmax(votes)]
submission_prognosis_list += [response]
submission_df = pd.DataFrame()
submission_df['id'] = test_df['id']
submission_df['prognosis'] = submission_prognosis_list
return submission_df
train_df.columns
X = train_df.to_numpy()[:, 1:-1]
X.shape
y = train_df['prognosis']
classes = sorted(list(train_df['prognosis'].unique()))
X.shape
validate_records = 4
clf_gbc = GradientBoostingClassifier(n_estimators=2500, learning_rate=0.001, max_features=30, max_depth=3, random_state=0, subsample=0.6).fit(X[validate_records:], y[validate_records:])
clf_nnc = MLPClassifier(random_state=0, hidden_layer_sizes=3, max_iter=1000).fit(X[validate_records:], y[validate_records:])
if validate_records > 0:
print(clf_nnc.score(X[:validate_records], y[:validate_records])) | code |
128023859/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128023859/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
train_df.columns
X = train_df.to_numpy()[:, 1:-1]
X.shape
X.shape | code |
128023859/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
def create_ensamble_submission():
submission_prognosis_list = []
for i in range(0, len(results_gbc)):
votes = np.zeros(11)
sorted = np.argsort(results_gbc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
sorted = np.argsort(results_svc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
sorted = np.argsort(results_nnc[i])
for ii in range(0, 11):
votes[sorted[ii]] += ii
response = classes[np.argmax(votes)]
votes[np.argmax(votes)] = 0
response += ' ' + classes[np.argmax(votes)]
votes[np.argmax(votes)] = 0
response += ' ' + classes[np.argmax(votes)]
submission_prognosis_list += [response]
submission_df = pd.DataFrame()
submission_df['id'] = test_df['id']
submission_df['prognosis'] = submission_prognosis_list
return submission_df
train_df.columns
X = train_df.to_numpy()[:, 1:-1]
X.shape
y = train_df['prognosis']
classes = sorted(list(train_df['prognosis'].unique()))
X.shape
validate_records = 4
clf_gbc = GradientBoostingClassifier(n_estimators=2500, learning_rate=0.001, max_features=30, max_depth=3, random_state=0, subsample=0.6).fit(X[validate_records:], y[validate_records:])
clf_svc = make_pipeline(StandardScaler(), SVC(gamma='auto', kernel='poly', probability=True))
clf_svc.fit(X, y)
if validate_records > 0:
print(clf_svc.score(X[:validate_records], y[:validate_records])) | code |
128023859/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
test_df | code |
128023859/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
train_df.columns
train_df | code |
50227784/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.head() | code |
50227784/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | plt.rcParams['figure.facecolor'] = 'white'
plt.rcParams['axes.facecolor'] = '#464646'
plt.rcParams['figure.figsize'] = (10, 7)
plt.rcParams['text.color'] = '#666666'
plt.rcParams['axes.labelcolor'] = '#666666'
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 16
plt.rcParams['xtick.color'] = '#666666'
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.color'] = '#666666'
plt.rcParams['ytick.labelsize'] = 14
sns.color_palette('dark')
tqdm.pandas() | code |
50227784/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Embedding, Dropout, Flatten, Layer, Input
from keras.models import Sequential, Model
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
Ytrain = Ytrain.map({'positive': 1, 'negative': 0})
Ycv = Ycv.map({'positive': 1, 'negative': 0})
tokenizer = Tokenizer(num_words=20000, oov_token='<UNK>')
tokenizer.fit_on_texts(Xtrain)
word2num = tokenizer.word_index
num2word = {k: w for w, k in word2num.items()}
train_sequences = tokenizer.texts_to_sequences(Xtrain)
maxlen = max([len(x) for x in train_sequences])
train_padded = pad_sequences(train_sequences, padding='post', truncating='post', maxlen=100)
test_sequences = tokenizer.texts_to_sequences(Xcv)
test_padded = pad_sequences(test_sequences, padding='post', truncating='post', maxlen=100)
inp = Input(shape=(100,))
x = Embedding(20000, 256, trainable=False)(inp)
x = Bidirectional(LSTM(300, return_sequences=True, dropout=0.25, recurrent_dropout=0.25))(x)
x = Attention()(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.25)(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(inputs=inp, outputs=x)
model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
file_path = 'model.hdf5'
ckpt = ModelCheckpoint(file_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
early = EarlyStopping(monitor='val_loss', mode='min', patience=1)
model.fit(train_padded, Ytrain, batch_size=1024, epochs=30, validation_data=(test_padded, Ycv), callbacks=[ckpt]) | code |
50227784/cell_2 | [
"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 |
50227784/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape
data_df['sentiment'].value_counts() | code |
50227784/cell_15 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from nltk.tokenize import word_tokenize
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)
import re
import string
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape
for i in range(10):
idx = np.random.randint(1, 50001)
def remove_break(text):
return re.sub('<br />', '', text)
def remove_punct(text):
nopunct = ''
for c in text:
if c not in string.punctuation:
nopunct = nopunct + c
return nopunct
def remove_numbers(text):
return re.sub('[0-9]', '', text)
def remove_links(text):
return re.sub('http\\S+', '', text)
def remove_stop_words(word_list):
stopwords_list = set(stopwords.words('english'))
word_list = [word for word in word_list if word not in stopwords_list]
return ' '.join(word_list)
def get_root(word_list):
ps = PorterStemmer()
return [ps.stem(word) for word in word_list]
def clean_text(text):
text = remove_break(text)
text = remove_links(text)
text = remove_numbers(text)
text = remove_punct(text)
word_list = word_tokenize(text)
word_list = get_root(word_list)
return ' '.join(word_list)
data_df['clean_review'] = data_df['review'].progress_apply(clean_text) | code |
50227784/cell_16 | [
"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)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape
for i in range(10):
idx = np.random.randint(1, 50001)
data_df.head() | code |
50227784/cell_3 | [
"text_plain_output_1.png"
] | !pwd | code |
50227784/cell_24 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Embedding, Dropout, Flatten, Layer, Input
from keras.models import Sequential, Model
inp = Input(shape=(100,))
x = Embedding(20000, 256, trainable=False)(inp)
x = Bidirectional(LSTM(300, return_sequences=True, dropout=0.25, recurrent_dropout=0.25))(x)
x = Attention()(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.25)(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(inputs=inp, outputs=x)
model.summary() | code |
50227784/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape | code |
50227784/cell_27 | [
"text_html_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import LSTM, Dense, TimeDistributed, Bidirectional, Embedding, Dropout, Flatten, Layer, Input
from keras.models import Sequential, Model
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from sklearn import metrics
from sklearn.metrics import precision_recall_curve, auc, roc_auc_score, roc_curve, recall_score
import seaborn as sns
Ytrain = Ytrain.map({'positive': 1, 'negative': 0})
Ycv = Ycv.map({'positive': 1, 'negative': 0})
tokenizer = Tokenizer(num_words=20000, oov_token='<UNK>')
tokenizer.fit_on_texts(Xtrain)
word2num = tokenizer.word_index
num2word = {k: w for w, k in word2num.items()}
train_sequences = tokenizer.texts_to_sequences(Xtrain)
maxlen = max([len(x) for x in train_sequences])
train_padded = pad_sequences(train_sequences, padding='post', truncating='post', maxlen=100)
test_sequences = tokenizer.texts_to_sequences(Xcv)
test_padded = pad_sequences(test_sequences, padding='post', truncating='post', maxlen=100)
inp = Input(shape=(100,))
x = Embedding(20000, 256, trainable=False)(inp)
x = Bidirectional(LSTM(300, return_sequences=True, dropout=0.25, recurrent_dropout=0.25))(x)
x = Attention()(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.25)(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(inputs=inp, outputs=x)
model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
file_path = 'model.hdf5'
ckpt = ModelCheckpoint(file_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
early = EarlyStopping(monitor='val_loss', mode='min', patience=1)
model.fit(train_padded, Ytrain, batch_size=1024, epochs=30, validation_data=(test_padded, Ycv), callbacks=[ckpt])
Ycv_pred0 = model.predict(test_padded)
Ycv_pred = (Ycv_pred0 > 0.5).astype('int64')
print('Accuracy :', metrics.accuracy_score(Ycv, Ycv_pred))
print('f1 score macro :', metrics.f1_score(Ycv, Ycv_pred, average='macro'))
print('f1 scoore micro :', metrics.f1_score(Ycv, Ycv_pred, average='micro'))
print('Hamming loss :', metrics.hamming_loss(Ycv, Ycv_pred))
fpr, tpr, thresh = roc_curve(Ycv, Ycv_pred0)
print('auc: ', auc(fpr, tpr))
print('Classification report: \n', metrics.classification_report(Ycv, Ycv_pred))
fig, ax = plt.subplots(figsize=[10, 7])
ax.set_title('Receiver Operating Characteristic trainning')
ax.plot(fpr, tpr, sns.xkcd_rgb['greenish cyan'])
ax.plot([0, 1], [0, 1], ls='--', c=sns.xkcd_rgb['red pink'])
ax.set_xlim([-0.01, 1.01])
ax.set_ylim([-0.01, 1.01])
ax.set_ylabel('True Positive Rate')
ax.set_xlabel('False Positive Rate') | code |
50227784/cell_12 | [
"application_vnd.jupyter.stderr_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)
data_df = pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')
data_df.shape
for i in range(10):
idx = np.random.randint(1, 50001)
print('Review {}:'.format(i + 1))
print('\n\n')
print(data_df.iloc[idx]['review'])
print('\n')
print('*' * 100)
print('\n') | code |
128042900/cell_13 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import numpy as np
import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape
X = sentiment_matrix
from sklearn.decomposition import TruncatedSVD
SVD = TruncatedSVD(n_components=10)
decomposed_matrix = SVD.fit_transform(X)
decomposed_matrix.shape
'\nThe singular value decomposition(SVD) provides another way to factorize a matrix, into singular vectors and singular values. ... The SVD is used widely both in the calculation of other matrix operations, such as matrix inverse, but also as a data reduction method in machine learning\n'
decomposed_matrix.shape
correlation_matrix = np.corrcoef(decomposed_matrix)
correlation_matrix
i = '0486413012'
product_names = list(X.index)
product_ID = product_names.index(i)
product_ID
correlation_product_ID = correlation_matrix[product_ID]
correlation_product_ID.shape | code |
128042900/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix | code |
128042900/cell_11 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import numpy as np
import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape
X = sentiment_matrix
from sklearn.decomposition import TruncatedSVD
SVD = TruncatedSVD(n_components=10)
decomposed_matrix = SVD.fit_transform(X)
decomposed_matrix.shape
'\nThe singular value decomposition(SVD) provides another way to factorize a matrix, into singular vectors and singular values. ... The SVD is used widely both in the calculation of other matrix operations, such as matrix inverse, but also as a data reduction method in machine learning\n'
decomposed_matrix.shape
correlation_matrix = np.corrcoef(decomposed_matrix)
correlation_matrix | code |
128042900/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
data = pd.read_csv('/kaggle/input/data-work/data_work')
data.query("asin == '0486413012'")
data.query("asin == '0005000009'")
data.query("asin == '0005092663'")
data.query("asin == '0310396336'") | code |
128042900/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 |
128042900/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape | code |
128042900/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
data = pd.read_csv('/kaggle/input/data-work/data_work')
data.query("asin == '0486413012'")
data.query("asin == '0005000009'")
data.query("asin == '0005092663'") | code |
128042900/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape
X = sentiment_matrix
X.head(20) | code |
128042900/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
new_pr.query("asin == '0310396336'") | code |
128042900/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
data = pd.read_csv('/kaggle/input/data-work/data_work')
data.query("asin == '0486413012'") | code |
128042900/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
data = pd.read_csv('/kaggle/input/data-work/data_work')
data.query("asin == '0486413012'")
data.query("asin == '0005000009'") | code |
128042900/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import numpy as np
import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape
X = sentiment_matrix
from sklearn.decomposition import TruncatedSVD
SVD = TruncatedSVD(n_components=10)
decomposed_matrix = SVD.fit_transform(X)
decomposed_matrix.shape
'\nThe singular value decomposition(SVD) provides another way to factorize a matrix, into singular vectors and singular values. ... The SVD is used widely both in the calculation of other matrix operations, such as matrix inverse, but also as a data reduction method in machine learning\n'
decomposed_matrix.shape
correlation_matrix = np.corrcoef(decomposed_matrix)
correlation_matrix
i = '0486413012'
product_names = list(X.index)
product_ID = product_names.index(i)
product_ID
correlation_product_ID = correlation_matrix[product_ID]
correlation_product_ID.shape
Recommend = list(X.index[correlation_product_ID > 0.65])
Recommend.remove(i)
Recommend[0:24] | code |
128042900/cell_10 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape
X = sentiment_matrix
from sklearn.decomposition import TruncatedSVD
SVD = TruncatedSVD(n_components=10)
decomposed_matrix = SVD.fit_transform(X)
decomposed_matrix.shape
'\nThe singular value decomposition(SVD) provides another way to factorize a matrix, into singular vectors and singular values. ... The SVD is used widely both in the calculation of other matrix operations, such as matrix inverse, but also as a data reduction method in machine learning\n'
decomposed_matrix.shape | code |
128042900/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
sentiment_matrix = new_pr.pivot_table(values='total_score', index='asin', columns='reviewerID', fill_value=0)
sentiment_matrix
sentiment_matrix.shape
X = sentiment_matrix
i = '0486413012'
product_names = list(X.index)
product_ID = product_names.index(i)
product_ID | code |
128042900/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
new_pr = pd.read_csv('/kaggle/input/collaborative-csv/data_collaborative_full_csv')
new_pr = new_pr.sample(50000)
new_pr | code |
1007980/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.decomposition import PCA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from time import time
import numpy as np
import pandas as pd
start = time()
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
trainData = train_data.drop('label', 1)
trainLabel = train_data[['label']]
header = trainData.columns
X_train, X_test, y_train, y_test = train_test_split(trainData, trainLabel, test_size=0.3, random_state=1)
norm = Normalizer().fit(X_train)
X_train = norm.transform(X_train)
X_test = norm.transform(X_test)
testData = norm.transform(test_data)
X_train = pd.DataFrame(X_train, columns=header)
X_test = pd.DataFrame(X_test, columns=header)
testData = pd.DataFrame(testData, columns=header)
y_train = y_train.as_matrix()
y_test = y_test.as_matrix()
end = time()
start = time()
component = 30
pca = PCA(n_components=component).fit(X_train)
X_train = pca.transform(X_train)
X_test = pca.transform(X_test)
testData = pca.transform(testData)
X_train = pd.DataFrame(X_train)
X_test = pd.DataFrame(X_test)
testData = pd.DataFrame(testData)
neighbors = 5
CLF1 = KNeighborsClassifier(n_neighbors=neighbors).fit(X_train, np.ravel(y_train))
penalty_C = 10.0
CLF2 = SVC(C=penalty_C, gamma=0.1, kernel='rbf').fit(X_train, np.ravel(y_train))
max_depth = 15
CLF3 = AdaBoostClassifier(DecisionTreeClassifier(max_depth=max_depth), n_estimators=1000, learning_rate=1.0, algorithm='SAMME.R').fit(X_train, np.ravel(y_train))
end = time()
predLabel1 = CLF1.predict(testData)
predLabel2 = CLF2.predict(testData)
predLabel3 = CLF3.predict(testData)
submission = pd.DataFrame({'ImageId': np.arange(1, predLabel1.shape[0] + 1), 'Label': predLabel1})
submission.to_csv('submission1_KNN.csv', index=False)
submission = pd.DataFrame({'ImageId': np.arange(1, predLabel2.shape[0] + 1), 'Label': predLabel2})
submission.to_csv('submission2_SVM.csv', index=False)
submission = pd.DataFrame({'ImageId': np.arange(1, predLabel3.shape[0] + 1), 'Label': predLabel3})
submission.to_csv('submission3_ADA.csv', index=False) | code |
1007980/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.decomposition import PCA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from time import time
import numpy as np
import pandas as pd
start = time()
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
trainData = train_data.drop('label', 1)
trainLabel = train_data[['label']]
header = trainData.columns
X_train, X_test, y_train, y_test = train_test_split(trainData, trainLabel, test_size=0.3, random_state=1)
norm = Normalizer().fit(X_train)
X_train = norm.transform(X_train)
X_test = norm.transform(X_test)
testData = norm.transform(test_data)
X_train = pd.DataFrame(X_train, columns=header)
X_test = pd.DataFrame(X_test, columns=header)
testData = pd.DataFrame(testData, columns=header)
y_train = y_train.as_matrix()
y_test = y_test.as_matrix()
end = time()
start = time()
component = 30
pca = PCA(n_components=component).fit(X_train)
X_train = pca.transform(X_train)
X_test = pca.transform(X_test)
testData = pca.transform(testData)
X_train = pd.DataFrame(X_train)
X_test = pd.DataFrame(X_test)
testData = pd.DataFrame(testData)
print('PCA - component : {}'.format(component))
neighbors = 5
CLF1 = KNeighborsClassifier(n_neighbors=neighbors).fit(X_train, np.ravel(y_train))
print('CLF | KNN-neighbors : {}'.format(neighbors))
penalty_C = 10.0
CLF2 = SVC(C=penalty_C, gamma=0.1, kernel='rbf').fit(X_train, np.ravel(y_train))
print('CLF | SVM-penalty_C : {}'.format(penalty_C))
max_depth = 15
CLF3 = AdaBoostClassifier(DecisionTreeClassifier(max_depth=max_depth), n_estimators=1000, learning_rate=1.0, algorithm='SAMME.R').fit(X_train, np.ravel(y_train))
print('CLF | Adaboost-max_depth : {}'.format(max_depth))
print('\n---CLF1---')
print('ACC: %f.4' % CLF1.score(X_test, y_test))
print('\n---CLF2---')
print('ACC: %f.4' % CLF2.score(X_test, y_test))
print('\n---CLF3---')
print('ACC: %f.4' % CLF3.score(X_test, y_test))
end = time()
print('\n***Training Done: %.2f ***\n' % (end - start)) | code |
1007980/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
from time import time
from sklearn.preprocessing import Normalizer
from sklearn.decomposition import PCA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import train_test_split | code |
1007980/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import Normalizer
from time import time
import pandas as pd
start = time()
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
trainData = train_data.drop('label', 1)
trainLabel = train_data[['label']]
header = trainData.columns
X_train, X_test, y_train, y_test = train_test_split(trainData, trainLabel, test_size=0.3, random_state=1)
norm = Normalizer().fit(X_train)
X_train = norm.transform(X_train)
X_test = norm.transform(X_test)
testData = norm.transform(test_data)
X_train = pd.DataFrame(X_train, columns=header)
X_test = pd.DataFrame(X_test, columns=header)
testData = pd.DataFrame(testData, columns=header)
y_train = y_train.as_matrix()
y_test = y_test.as_matrix()
end = time()
print('\n***Loading Done: %.2f ***\n' % (end - start)) | code |
105199186/cell_9 | [
"text_plain_output_1.png"
] | unig_dist = {'apple': 0.023, 'bee': 0.12, 'desk': 0.34, 'chair': 0.517}
sum(unig_dist.values()) | code |
105199186/cell_11 | [
"text_plain_output_1.png"
] | unig_dist = {'apple': 0.023, 'bee': 0.12, 'desk': 0.34, 'chair': 0.517}
sum(unig_dist.values())
alpha = 3 / 4
noise_dist = {key: val ** alpha for key, val in unig_dist.items()}
Z = sum(noise_dist.values())
noise_dist_normalized = {key: val / Z for key, val in noise_dist.items()}
noise_dist_normalized
sum(noise_dist_normalized.values()) | code |
105199186/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
unig_dist = {'apple': 0.023, 'bee': 0.12, 'desk': 0.34, 'chair': 0.517}
sum(unig_dist.values())
alpha = 3 / 4
noise_dist = {key: val ** alpha for key, val in unig_dist.items()}
Z = sum(noise_dist.values())
noise_dist_normalized = {key: val / Z for key, val in noise_dist.items()}
noise_dist_normalized
sum(noise_dist_normalized.values())
K = 10
np.random.choice(list(noise_dist_normalized.keys()), size=K, p=list(noise_dist_normalized.values())) | code |
105199186/cell_10 | [
"image_output_1.png"
] | unig_dist = {'apple': 0.023, 'bee': 0.12, 'desk': 0.34, 'chair': 0.517}
sum(unig_dist.values())
alpha = 3 / 4
noise_dist = {key: val ** alpha for key, val in unig_dist.items()}
Z = sum(noise_dist.values())
noise_dist_normalized = {key: val / Z for key, val in noise_dist.items()}
noise_dist_normalized | code |
105199186/cell_5 | [
"text_plain_output_1.png"
] | from IPython.display import Image
Image('../input/noise-distpng/noise_dist.png') | code |
74058017/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
y_train = train['claim']
X_train = train.drop(columns='claim')
from sklearn.feature_selection import mutual_info_classif
from sklearn.impute import SimpleImputer
numerical_transformer = SimpleImputer(strategy='constant', fill_value=0)
imputed_X_train = pd.DataFrame(numerical_transformer.fit_transform(X_train))
imputed_X_train.columns = X_train.columns
cols_with_missing = [col for col in imputed_X_train.columns if imputed_X_train[col].isnull().any()]
print(cols_with_missing) | code |
74058017/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
y_train = train['claim']
X_train = train.drop(columns='claim')
from sklearn.feature_selection import mutual_info_classif
from sklearn.impute import SimpleImputer
numerical_transformer = SimpleImputer(strategy='constant', fill_value=0)
imputed_X_train = pd.DataFrame(numerical_transformer.fit_transform(X_train))
imputed_X_train.columns = X_train.columns
cols_with_missing = [col for col in imputed_X_train.columns if imputed_X_train[col].isnull().any()]
model = RandomForestClassifier(n_estimators=10, max_samples=100000, n_jobs=4)
model.fit(imputed_X_train, y_train) | code |
74052853/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
print(f'Number of rows in training dataset------------->{train_row}\nNumber of columns in training dataset---------->{train_col}\n')
print(f'Number of rows in testing dataset-------------->{test_row}\nNumber of columns in testing dataset----------->{test_col}') | code |
74052853/cell_26 | [
"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
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.corr()
train.corrwith(train['claim'])
plot , ax = plt.subplots(figsize=(10,8))
sns.heatmap(train.corr())
plot, ax = plt.subplots(figsize=(10, 8))
sns.countplot(train['claim']) | code |
74052853/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train.head() | code |
74052853/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.corr()
train.corrwith(train['claim']) | code |
74052853/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 |
74052853/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.corr() | code |
74052853/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.corr()
train.corrwith(train['claim'])
plot , ax = plt.subplots(figsize=(10,8))
sns.heatmap(train.corr())
hist = train.hist(bins = 25, figsize=(70,45))
features = train.columns.tolist()[0:-1]
target = ['claim']
train_colum_missing = train.isnull().sum()
train_row_missing = train[features].isnull().sum(axis=1)
train['no_of_missing_data'] = train_row_missing
test_colum_missing = test.isnull().sum()
test_row_missing = test[features].isnull().sum(axis=1)
test['no_of_missing_data'] = test_row_missing
print(f'Total number of missing values in training dataset---->{train_total_missing}')
print(f'Total number of missing values in testing dataset----->{test_total_missing}')
train_no_of_missing_rows = (train['no_of_missing_data'] != 0).sum()
print('\n{0:{fill}{align}80}\n'.format('Training Data', fill='=', align='^'))
print(f"Total rows -----------------------> {train_row}\nNumber of rows has missing data---> {train_no_of_missing_rows}\n{'-' * 50}\nNumber of rows has full data--------> {train_row - train_no_of_missing_rows}")
test_no_of_missing_rows = (test['no_of_missing_data'] != 0).sum()
print('\n{0:{fill}{align}80}\n'.format('Testing Data', fill='=', align='^'))
print(f"Total rows -----------------------> {test_row}\nNumber of rows has missing data---> {test_no_of_missing_rows}\n{'-' * 50}\nNumber of rows has full data--------> {test_row - test_no_of_missing_rows}") | code |
74052853/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.describe() | code |
74052853/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.corr()
train.corrwith(train['claim'])
plot, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(train.corr()) | code |
74052853/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
print(train.info())
print('=' * 50)
test.info() | code |
74052853/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
test.describe() | code |
74052853/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
train_row, train_col = train.shape
test_row, test_col = test.shape
train.corr()
train.corrwith(train['claim'])
plot , ax = plt.subplots(figsize=(10,8))
sns.heatmap(train.corr())
hist = train.hist(bins=25, figsize=(70, 45)) | code |
74052853/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col=0)
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col=0)
test.head() | code |
128004723/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)
import warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=pd.errors.PerformanceWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
submission_df = pd.read_csv('/kaggle/input/playground-series-s3e13/sample_submission.csv')
train_df = train_df.set_index('id')
test_df = test_df.set_index('id')
test_df.head(3) | code |
128004723/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)
import warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=pd.errors.PerformanceWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
submission_df = pd.read_csv('/kaggle/input/playground-series-s3e13/sample_submission.csv')
train_df = train_df.set_index('id')
test_df = test_df.set_index('id')
def convert_to_string(row):
row_dict = row.to_dict()
base = 'a person with the symptoms '
for symptom, value in row_dict.items():
if symptom == 'prognosis':
continue
elif value == 1:
base += symptom.replace('_', ' ') + ', '
base = base.rstrip(', ')
return base
train_df['text_symptom'] = train_df.apply(convert_to_string, axis=1)
test_df['text_symptom'] = test_df.apply(convert_to_string, axis=1)
train_df['prognosis'].head(5).values | code |
128004723/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 |
128004723/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=pd.errors.PerformanceWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
submission_df = pd.read_csv('/kaggle/input/playground-series-s3e13/sample_submission.csv')
train_df = train_df.set_index('id')
test_df = test_df.set_index('id')
def convert_to_string(row):
row_dict = row.to_dict()
base = 'a person with the symptoms '
for symptom, value in row_dict.items():
if symptom == 'prognosis':
continue
elif value == 1:
base += symptom.replace('_', ' ') + ', '
base = base.rstrip(', ')
return base
train_df['text_symptom'] = train_df.apply(convert_to_string, axis=1)
test_df['text_symptom'] = test_df.apply(convert_to_string, axis=1)
train_df['text_symptom'].head(5).values | code |
128004723/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=pd.errors.PerformanceWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
train_df = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
submission_df = pd.read_csv('/kaggle/input/playground-series-s3e13/sample_submission.csv')
train_df = train_df.set_index('id')
test_df = test_df.set_index('id')
train_df.head(3) | code |
88085166/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
def mil_to_thousand(data):
regex = '[-+]?(\\d*\\.?\\d+)'
data = str(data)
size = len(data)
if data[size - 1] == 'M':
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
data = data * 1000
else:
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
return data
df['Release Clause'] = df['Release Clause'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Release Clause')
df['Release Clause'] = df['Release Clause']
players_rc = df[['Name', 'Release Clause']].sort_values(by='Release Clause', ascending=False)[:20]
horizontal_bar(players_rc, 'Release Clause', 'Name', 'Players with Highest Release Clause (in thousand euros)')
df['Value'] = df['Value'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Value')
df['Value'] = df['Value'].astype('float')
pos_value = df.groupby('Position')['Value'].sum().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Total Value in Every Position (in thousand euros)')
pos_value = df.groupby('Position')['Value'].median().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Median Value in Every Position (in thousand euros)')
top20_nationality = df.groupby('Nationality')['ID'].count().sort_values(ascending=False).reset_index()[:20]
top20_nationality = top20_nationality.rename({'ID': 'num_of_player'}, axis=1)
horizontal_bar(top20_nationality, 'num_of_player', 'Nationality', 'Top 20 Players Nationality')
nationality_ovr_med = df.groupby('Nationality')['Overall'].median()
nationality_num_of_players = df.groupby('Nationality')['ID'].count()
nationality_df = pd.DataFrame({'num of player': nationality_num_of_players, 'mean overall': nationality_ovr_med})
nationality_df = nationality_df[nationality_df['num of player'] > 100]
nationality_df = nationality_df.sort_values(by='mean overall', ascending=False)
nationality_df = nationality_df.reset_index()[:20]
horizontal_bar(nationality_df, 'mean overall', 'Nationality', 'Nationality with Highest Median Players Overall Ratings')
league_ovr = df.groupby('Club')['Overall'].median().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(league_ovr, 'Overall', 'Club', 'Clubs with Highest Median Overall Ratings')
most_valuable_club = df.groupby('Club')['Value'].sum().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(most_valuable_club, 'Value', 'Club', 'Total Value Players in Every Clubs (in thousand eruos)') | code |
88085166/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
make_distplot(df['Overall'], 'Player Overall Distribution') | code |
88085166/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.describe() | code |
88085166/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
def mil_to_thousand(data):
regex = '[-+]?(\\d*\\.?\\d+)'
data = str(data)
size = len(data)
if data[size - 1] == 'M':
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
data = data * 1000
else:
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
return data
df['Release Clause'] = df['Release Clause'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Release Clause')
df['Release Clause'] = df['Release Clause']
players_rc = df[['Name', 'Release Clause']].sort_values(by='Release Clause', ascending=False)[:20]
horizontal_bar(players_rc, 'Release Clause', 'Name', 'Players with Highest Release Clause (in thousand euros)')
df['Value'] = df['Value'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Value')
df['Value'] = df['Value'].astype('float')
pos_value = df.groupby('Position')['Value'].sum().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Total Value in Every Position (in thousand euros)')
pos_value = df.groupby('Position')['Value'].median().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Median Value in Every Position (in thousand euros)')
top20_nationality = df.groupby('Nationality')['ID'].count().sort_values(ascending=False).reset_index()[:20]
top20_nationality = top20_nationality.rename({'ID': 'num_of_player'}, axis=1)
horizontal_bar(top20_nationality, 'num_of_player', 'Nationality', 'Top 20 Players Nationality')
nationality_ovr_med = df.groupby('Nationality')['Overall'].median()
nationality_num_of_players = df.groupby('Nationality')['ID'].count()
nationality_df = pd.DataFrame({'num of player': nationality_num_of_players, 'mean overall': nationality_ovr_med})
nationality_df = nationality_df[nationality_df['num of player'] > 100]
nationality_df = nationality_df.sort_values(by='mean overall', ascending=False)
nationality_df = nationality_df.reset_index()[:20]
horizontal_bar(nationality_df, 'mean overall', 'Nationality', 'Nationality with Highest Median Players Overall Ratings')
league_ovr = df.groupby('Club')['Overall'].median().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(league_ovr, 'Overall', 'Club', 'Clubs with Highest Median Overall Ratings') | code |
88085166/cell_2 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.head() | code |
88085166/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
high_player_ovr = df[['Name', 'Overall']].sort_values(by='Overall', ascending=False).reset_index(drop=True)[:20]
horizontal_bar(high_player_ovr, 'Overall', 'Name', 'Players With Highest Overall Ratings') | code |
88085166/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
def mil_to_thousand(data):
regex = '[-+]?(\\d*\\.?\\d+)'
data = str(data)
size = len(data)
if data[size - 1] == 'M':
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
data = data * 1000
else:
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
return data
df['Release Clause'] = df['Release Clause'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Release Clause')
df['Release Clause'] = df['Release Clause']
players_rc = df[['Name', 'Release Clause']].sort_values(by='Release Clause', ascending=False)[:20]
horizontal_bar(players_rc, 'Release Clause', 'Name', 'Players with Highest Release Clause (in thousand euros)')
df['Value'] = df['Value'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Value')
df['Value'] = df['Value'].astype('float')
pos_value = df.groupby('Position')['Value'].sum().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Total Value in Every Position (in thousand euros)')
pos_value = df.groupby('Position')['Value'].median().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Median Value in Every Position (in thousand euros)')
top20_nationality = df.groupby('Nationality')['ID'].count().sort_values(ascending=False).reset_index()[:20]
top20_nationality = top20_nationality.rename({'ID': 'num_of_player'}, axis=1)
horizontal_bar(top20_nationality, 'num_of_player', 'Nationality', 'Top 20 Players Nationality')
nationality_ovr_med = df.groupby('Nationality')['Overall'].median()
nationality_num_of_players = df.groupby('Nationality')['ID'].count()
nationality_df = pd.DataFrame({'num of player': nationality_num_of_players, 'mean overall': nationality_ovr_med})
nationality_df = nationality_df[nationality_df['num of player'] > 100]
nationality_df = nationality_df.sort_values(by='mean overall', ascending=False)
nationality_df = nationality_df.reset_index()[:20]
horizontal_bar(nationality_df, 'mean overall', 'Nationality', 'Nationality with Highest Median Players Overall Ratings') | code |
88085166/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
def mil_to_thousand(data):
regex = '[-+]?(\\d*\\.?\\d+)'
data = str(data)
size = len(data)
if data[size - 1] == 'M':
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
data = data * 1000
else:
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
return data
df['Release Clause'] = df['Release Clause'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Release Clause')
df['Release Clause'] = df['Release Clause']
players_rc = df[['Name', 'Release Clause']].sort_values(by='Release Clause', ascending=False)[:20]
horizontal_bar(players_rc, 'Release Clause', 'Name', 'Players with Highest Release Clause (in thousand euros)')
df['Value'] = df['Value'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Value')
df['Value'] = df['Value'].astype('float')
pos_value = df.groupby('Position')['Value'].sum().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Total Value in Every Position (in thousand euros)')
pos_value = df.groupby('Position')['Value'].median().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Median Value in Every Position (in thousand euros)')
top20_nationality = df.groupby('Nationality')['ID'].count().sort_values(ascending=False).reset_index()[:20]
top20_nationality = top20_nationality.rename({'ID': 'num_of_player'}, axis=1)
horizontal_bar(top20_nationality, 'num_of_player', 'Nationality', 'Top 20 Players Nationality') | code |
88085166/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
make_distplot(df['Age'], 'Player Age Distribution') | code |
88085166/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
def mil_to_thousand(data):
regex = '[-+]?(\\d*\\.?\\d+)'
data = str(data)
size = len(data)
if data[size - 1] == 'M':
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
data = data * 1000
else:
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
return data
df['Release Clause'] = df['Release Clause'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Release Clause')
df['Release Clause'] = df['Release Clause']
players_rc = df[['Name', 'Release Clause']].sort_values(by='Release Clause', ascending=False)[:20]
horizontal_bar(players_rc, 'Release Clause', 'Name', 'Players with Highest Release Clause (in thousand euros)')
df['Value'] = df['Value'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Value')
df['Value'] = df['Value'].astype('float')
pos_value = df.groupby('Position')['Value'].sum().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Total Value in Every Position (in thousand euros)') | code |
88085166/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
def mil_to_thousand(data):
regex = '[-+]?(\\d*\\.?\\d+)'
data = str(data)
size = len(data)
if data[size - 1] == 'M':
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
data = data * 1000
else:
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
return data
df['Release Clause'] = df['Release Clause'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Release Clause')
df['Release Clause'] = df['Release Clause']
players_rc = df[['Name', 'Release Clause']].sort_values(by='Release Clause', ascending=False)[:20]
horizontal_bar(players_rc, 'Release Clause', 'Name', 'Players with Highest Release Clause (in thousand euros)')
df['Value'] = df['Value'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Value')
df['Value'] = df['Value'].astype('float')
pos_value = df.groupby('Position')['Value'].sum().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Total Value in Every Position (in thousand euros)')
pos_value = df.groupby('Position')['Value'].median().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Median Value in Every Position (in thousand euros)') | code |
88085166/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.info() | code |
88085166/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
def mil_to_thousand(data):
regex = '[-+]?(\\d*\\.?\\d+)'
data = str(data)
size = len(data)
if data[size - 1] == 'M':
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
data = data * 1000
else:
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
return data
df['Release Clause'] = df['Release Clause'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Release Clause')
df['Release Clause'] = df['Release Clause']
players_rc = df[['Name', 'Release Clause']].sort_values(by='Release Clause', ascending=False)[:20]
horizontal_bar(players_rc, 'Release Clause', 'Name', 'Players with Highest Release Clause (in thousand euros)')
df['Value'] = df['Value'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Value')
df['Value'] = df['Value'].astype('float')
pos_value = df.groupby('Position')['Value'].sum().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Total Value in Every Position (in thousand euros)')
pos_value = df.groupby('Position')['Value'].median().sort_values(ascending=False).reset_index()[:20]
horizontal_bar(pos_value, 'Value', 'Position', 'Median Value in Every Position (in thousand euros)')
high_young_player_ovr = df[df['Age'] <= 20][['Name', 'Overall']].sort_values(by='Overall', ascending=False).reset_index(drop=True)[:20]
horizontal_bar(high_young_player_ovr, 'Overall', 'Name', 'Players under 20 Year Old with Highest Overall Ratings') | code |
88085166/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
def mil_to_thousand(data):
regex = '[-+]?(\\d*\\.?\\d+)'
data = str(data)
size = len(data)
if data[size - 1] == 'M':
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
data = data * 1000
else:
data = re.findall(regex, data)
data = np.asarray(data).astype('float64')
return data
df['Release Clause'] = df['Release Clause'].apply(lambda x: mil_to_thousand(x))
df = df.explode('Release Clause')
df['Release Clause'] = df['Release Clause']
players_rc = df[['Name', 'Release Clause']].sort_values(by='Release Clause', ascending=False)[:20]
horizontal_bar(players_rc, 'Release Clause', 'Name', 'Players with Highest Release Clause (in thousand euros)') | code |
88085166/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
def make_distplot(data,title):
fig,ax=plt.subplots(figsize=(10,5))
ax.set_title(title)
sns.distplot(data,bins=30)
def horizontal_bar(data,x,y,title):
fig,ax=plt.subplots(figsize=(10,5))
sns.barplot(data=data,x=x,y=y,color='steelblue')
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.set_title(title)
df['Wage'] = df['Wage'].str.extract('(\\d+)')
df['Wage'] = df['Wage'].astype('float64')
highest_wage = df[['Name', 'Wage']].sort_values(by='Wage', ascending=False)[:20]
horizontal_bar(highest_wage, 'Wage', 'Name', 'Players with Highest Wage') | code |
88085166/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
percent_null = []
n_col = df.columns
for col in n_col:
percent_null.append(df[col].isnull().sum() / len(df[col]) * 100)
df_missing = pd.DataFrame(percent_null, index=df.columns, columns=['percent_missing'])
df_missing = df_missing.reset_index().rename({'index': 'column_name'}, axis=1)
df_missing = df_missing[df_missing['percent_missing'] > 0].sort_values(by='percent_missing', ascending=False)
clr = ['steelblue' if percent > 10 else 'skyblue' for percent in df_missing['percent_missing']]
plt.figure(figsize=(10, 20))
sns.barplot(data=df_missing, x='percent_missing', y='column_name', palette=clr) | code |
128023684/cell_21 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_third.columns
df_third_part = df_third[df_third.Rate > 0].reset_index()
df_combined = pd.merge(df_third_part, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
combined_pair = df_combined[['prod_cat', 'Store_type']].drop_duplicates().to_dict('records')
contribution_dictionary = {}
for j in range(len(combined_pair)):
df_sub_part = df_combined[(df_combined.prod_cat == combined_pair[j]['prod_cat']) & (df_combined.Store_type == combined_pair[j]['Store_type'])].reset_index()
contribution_dictionary[combined_pair[j]['prod_cat'], combined_pair[j]['Store_type']] = np.round(100 * df_sub_part['total_amt'].sum() / df_combined['total_amt'].sum(), 2)
import operator
sorted_x = {k: v for k, v in sorted(contribution_dictionary.items(), key=lambda item: item[1], reverse=True)}
sorted_x | code |
128023684/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_third.columns | code |
128023684/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
df_combined | code |
128023684/cell_23 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df_third.groupby(['cust_id', 'prod_cat_code', 'prod_subcat_code'])['transaction_id'].count().to_dict()
df_combined = pd.merge(df_third, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
grp_df = df_combined.groupby(['cust_id', 'prod_cat', 'prod_subcat', 'tran_date'])['tran_date', 'prod_subcat'].count()
df_third.columns
df_third_part = df_third[df_third.Rate > 0].reset_index()
df_combined = pd.merge(df_third_part, df_first, left_on=['prod_cat_code', 'prod_subcat_code'], right_on=['prod_cat_code', 'prod_sub_cat_code'], how='left')
md = pd.DataFrame(df_combined[['cust_id', 'prod_cat', 'prod_subcat', 'tran_date']].value_counts())
combined_pair = df_combined[['prod_cat', 'Store_type']].drop_duplicates().to_dict('records')
contribution_dictionary = {}
for j in range(len(combined_pair)):
df_sub_part = df_combined[(df_combined.prod_cat == combined_pair[j]['prod_cat']) & (df_combined.Store_type == combined_pair[j]['Store_type'])].reset_index()
contribution_dictionary[combined_pair[j]['prod_cat'], combined_pair[j]['Store_type']] = np.round(100 * df_sub_part['total_amt'].sum() / df_combined['total_amt'].sum(), 2)
import operator
sorted_x = {k: v for k, v in sorted(contribution_dictionary.items(), key=lambda item: item[1], reverse=True)}
df_pie = pd.DataFrame({'item and channel': list(sorted_x.keys()), 'contribution': list(sorted_x.values())}, index=list(sorted_x.keys()))
df_pie.plot(kind='pie', legend=None, y='contribution', autopct='%1.0f%%', figsize=(10, 20)) | code |
128023684/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_first | code |
128023684/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_second.keys() | code |
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