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121154415/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
import matplotlib.pyplot as plt
from copy import deepcopy
import warnings
warnings.filterwarnings('ignore')
import random
random.seed(42)
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121154415/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
data['upd23b_clinical_state_on_medication'].unique() | code |
121154415/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
df_proteins.info() | code |
121154415/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data | code |
121154415/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
data.loc[data.upd23b_clinical_state_on_medication == 'On', 'upd23b_clinical_state_on_medication'] = 1
data.loc[data.upd23b_clinical_state_on_medication == 'Off', 'upd23b_clinical_state_on_medication'] = 0
data['upd23b_clinical_state_on_medication'].value_counts() | code |
121154415/cell_35 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
data.loc[data.upd23b_clinical_state_on_medication == 'On', 'upd23b_clinical_state_on_medication'] = 1
data.loc[data.upd23b_clinical_state_on_medication == 'Off', 'upd23b_clinical_state_on_medication'] = 0
data['upd23b_clinical_state_on_medication'].unique() | code |
121154415/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
ppp = pd.DataFrame(df_proteins.groupby('UniProt').patient_id.nunique()).rename(columns={'patient_id': 'count_patient'}).reset_index().sort_values('count_patient', ascending=False)
ppp.head(10) | code |
121154415/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
df_cd.head() | code |
121154415/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
df_cd.info() | code |
121154415/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
ppp = pd.DataFrame(df_proteins.groupby('UniProt').patient_id.nunique()).rename(columns={'patient_id': 'count_patient'}).reset_index().sort_values('count_patient', ascending=False)
sns.histplot(ppp) | code |
121154415/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
data.loc[data.upd23b_clinical_state_on_medication == 'On', 'upd23b_clinical_state_on_medication'] = 1
data.loc[data.upd23b_clinical_state_on_medication == 'Off', 'upd23b_clinical_state_on_medication'] = 0
data['upd23b_clinical_state_on_medication'].mode() | code |
121154415/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
df_proteins.head() | code |
121154415/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
print('df_proteins: ', df_proteins.shape)
print('df_peptides: ', df_peptides.shape)
print('df_cd: ', df_cd.shape) | code |
122248445/cell_13 | [
"text_plain_output_1.png"
] | !pip install scikit-learn | code |
122248445/cell_9 | [
"text_plain_output_1.png"
] | import os
import shutil
MAIN_DIR = '/kaggle/input/bach-breast-cancer-histology-images/ICIAR2018_BACH_Challenge/ICIAR2018_BACH_Challenge/Photos'
BASE_DIR = '/kaggle/working/bach-train'
TRAIN_DIR = os.path.join(BASE_DIR, 'training')
VAL_DIR = os.path.join(BASE_DIR, 'validation')
TEST_DIR = os.path.join(BASE_DIR, 'test')
if os.path.exists(BASE_DIR):
shutil.rmtree(BASE_DIR)
os.makedirs(BASE_DIR)
os.makedirs(TRAIN_DIR)
os.makedirs(VAL_DIR)
os.makedirs(TEST_DIR)
bc_types = [file for file in os.listdir(MAIN_DIR) if os.path.isdir(os.path.join(MAIN_DIR, file))]
print('Types: ', bc_types)
bc_dict = {'InSitu': 2, 'Benign': 1, 'Normal': 0, 'Invasive': 3}
print('Encode: ', bc_dict)
dict_bc = {0: 'Normal', 1: 'Benign', 2: 'InSitu', 3: 'Invasive'}
print('Decode: ', dict_bc) | code |
122248445/cell_4 | [
"text_plain_output_1.png"
] | import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Device:', tpu.master())
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except:
strategy = tf.distribute.get_strategy()
print('Number of replicas:', strategy.num_replicas_in_sync) | code |
122248445/cell_2 | [
"text_html_output_1.png"
] | !apt-get update && apt-get install -y python3-opencv
!pip install opencv-python
!pip install seaborn
import cv2
import seaborn as sns | code |
122248445/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/init-bach-eda/bach_data.csv', index_col=0)
df.head() | code |
122248445/cell_15 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import shutil
MAIN_DIR = '/kaggle/input/bach-breast-cancer-histology-images/ICIAR2018_BACH_Challenge/ICIAR2018_BACH_Challenge/Photos'
BASE_DIR = '/kaggle/working/bach-train'
TRAIN_DIR = os.path.join(BASE_DIR, 'training')
VAL_DIR = os.path.join(BASE_DIR, 'validation')
TEST_DIR = os.path.join(BASE_DIR, 'test')
if os.path.exists(BASE_DIR):
shutil.rmtree(BASE_DIR)
os.makedirs(BASE_DIR)
os.makedirs(TRAIN_DIR)
os.makedirs(VAL_DIR)
os.makedirs(TEST_DIR)
bc_types = [file for file in os.listdir(MAIN_DIR) if os.path.isdir(os.path.join(MAIN_DIR, file))]
bc_dict = {'InSitu': 2, 'Benign': 1, 'Normal': 0, 'Invasive': 3}
dict_bc = {0: 'Normal', 1: 'Benign', 2: 'InSitu', 3: 'Invasive'}
for bc in bc_types:
train_type_folder = os.path.join(TRAIN_DIR, bc)
val_type_folder = os.path.join(VAL_DIR, bc)
test_type_folder = os.path.join(TEST_DIR, bc)
os.makedirs(train_type_folder)
os.makedirs(val_type_folder)
os.makedirs(test_type_folder)
df = pd.read_csv('/kaggle/input/init-bach-eda/bach_data.csv', index_col=0)
df['type'] = df['type'].map(bc_dict)
from sklearn.model_selection import train_test_split
type_dict = dict({0: list(), 1: list(), 2: list(), 3: list()})
for i in range(len(df)):
path = df['path'][i]
type_ = df['type'][i]
if os.path.getsize(path):
type_dict[type_].append(path)
(len(type_dict[0]), len(type_dict[1]), len(type_dict[2]), len(type_dict[3])) | code |
122248445/cell_3 | [
"text_plain_output_1.png"
] | import tensorflow as tf
from tensorflow.keras.preprocessing.image import img_to_array, ImageDataGenerator, load_img
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten, BatchNormalization
from tensorflow.keras.activations import softmax
from tensorflow.keras.optimizers import SGD, Adamax | code |
122248445/cell_17 | [
"text_html_output_1.png"
] | print('Training size: ', len(x_train), len(y_train))
print('Val size: ', len(x_val), len(y_val))
print('Test size: ', len(x_test), len(y_test)) | code |
122248445/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
import shutil
MAIN_DIR = '/kaggle/input/bach-breast-cancer-histology-images/ICIAR2018_BACH_Challenge/ICIAR2018_BACH_Challenge/Photos'
BASE_DIR = '/kaggle/working/bach-train'
TRAIN_DIR = os.path.join(BASE_DIR, 'training')
VAL_DIR = os.path.join(BASE_DIR, 'validation')
TEST_DIR = os.path.join(BASE_DIR, 'test')
if os.path.exists(BASE_DIR):
shutil.rmtree(BASE_DIR)
os.makedirs(BASE_DIR)
os.makedirs(TRAIN_DIR)
os.makedirs(VAL_DIR)
os.makedirs(TEST_DIR)
bc_types = [file for file in os.listdir(MAIN_DIR) if os.path.isdir(os.path.join(MAIN_DIR, file))]
bc_dict = {'InSitu': 2, 'Benign': 1, 'Normal': 0, 'Invasive': 3}
dict_bc = {0: 'Normal', 1: 'Benign', 2: 'InSitu', 3: 'Invasive'}
df = pd.read_csv('/kaggle/input/init-bach-eda/bach_data.csv', index_col=0)
df['type'] = df['type'].map(bc_dict)
df.head() | code |
122254156/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('nba_games.csv', index_col=0)
df | code |
89138263/cell_20 | [
"text_plain_output_1.png"
] | 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)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
train_df = train_df.rename(columns=lambda x: ''.join(x.split('_')))
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
test_df = test_df.rename(columns=lambda x: ''.join(x.split('_')))
def reduce_mem_usage(df, verbose=True):
numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
start_mem = df.memory_usage().sum() / 1024**2
for col in df.columns:
col_type = df[col].dtypes
if col_type in numerics:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int8)
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int16)
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
else:
if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
df[col] = df[col].astype(np.float16)
elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
df[col] = df[col].astype(np.float32)
else:
df[col] = df[col].astype(np.float64)
end_mem = df.memory_usage().sum() / 1024**2
if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))
return df
def grab_col_names(dataframe, cat_th=61, car_th=74):
"""
It gives the names of categorical, numerical and categorical but cardinal variables in the data set.
Note: Categorical variables with numerical appearance are also included in categorical variables.
Parameters
------
dataframe: dataframe
The dataframe from which variable names are to be retrieved
cat_th: int, optional
Class threshold value for numeric but categorical variables
car_th: int, optinal
Class threshold for categorical but cardinal variables
Returns
------
cat_cols: list
Categorical variable list
num_cols: list
Numerical variable list
cat_but_car: list
Categorical view cardinal variable list
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = the total number of variables
num_but_cat is inside cat_cols.
The sum of 3 lists with return is equal to the total number of variables: cat_cols + num_cols + cat_but_car = number of variables
"""
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == 'O']
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes != 'O']
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and dataframe[col].dtypes == 'O']
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != 'O']
num_cols = [col for col in num_cols if col not in num_but_cat]
return (cat_cols, num_cols, cat_but_car)
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
if na_name:
return (na_columns, missing_df)
na_cols, missing_df = missing_values_table(train_df, True)
missing_df.reset_index(inplace=True) | code |
89138263/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
train_df = train_df.rename(columns=lambda x: ''.join(x.split('_')))
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
test_df = test_df.rename(columns=lambda x: ''.join(x.split('_')))
for i in train_df.columns:
if train_df[i].dtypes == 'int64' or train_df[i].dtypes == 'float64':
train_df[i].fillna(train_df[i].mean(), inplace=True)
for i in train_df.columns:
if train_df[i].dtypes == 'object':
train_df[i].fillna(train_df[i].mode()[0], inplace=True)
train_df.isnull().sum() | code |
89138263/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
train_df = train_df.rename(columns=lambda x: ''.join(x.split('_')))
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
test_df = test_df.rename(columns=lambda x: ''.join(x.split('_')))
def grab_col_names(dataframe, cat_th=61, car_th=74):
"""
It gives the names of categorical, numerical and categorical but cardinal variables in the data set.
Note: Categorical variables with numerical appearance are also included in categorical variables.
Parameters
------
dataframe: dataframe
The dataframe from which variable names are to be retrieved
cat_th: int, optional
Class threshold value for numeric but categorical variables
car_th: int, optinal
Class threshold for categorical but cardinal variables
Returns
------
cat_cols: list
Categorical variable list
num_cols: list
Numerical variable list
cat_but_car: list
Categorical view cardinal variable list
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = the total number of variables
num_but_cat is inside cat_cols.
The sum of 3 lists with return is equal to the total number of variables: cat_cols + num_cols + cat_but_car = number of variables
"""
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == 'O']
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes != 'O']
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and dataframe[col].dtypes == 'O']
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != 'O']
num_cols = [col for col in num_cols if col not in num_but_cat]
return (cat_cols, num_cols, cat_but_car)
for i in train_df.columns:
if train_df[i].dtypes == 'int64' or train_df[i].dtypes == 'float64':
train_df[i].fillna(train_df[i].mean(), inplace=True)
for i in train_df.columns:
if train_df[i].dtypes == 'object':
train_df[i].fillna(train_df[i].mode()[0], inplace=True)
cat_cols, num_cols, cat_but_car = grab_col_names(train_df) | code |
89138263/cell_2 | [
"text_plain_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, cross_validate
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, VotingClassifier
from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler, RobustScaler
from sklearn.impute import KNNImputer
from sklearn.linear_model import LogisticRegression, LinearRegression, Ridge, Lasso, ElasticNet
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error
import warnings
from sklearn.exceptions import ConvergenceWarning
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, VotingRegressor
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier, AdaBoostClassifier
from sklearn.model_selection import cross_validate, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neighbors import LocalOutlierFactor
from sklearn.metrics import classification_report | code |
89138263/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 |
89138263/cell_47 | [
"text_plain_output_1.png"
] | 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)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
train_df = train_df.rename(columns=lambda x: ''.join(x.split('_')))
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
test_df = test_df.rename(columns=lambda x: ''.join(x.split('_')))
def reduce_mem_usage(df, verbose=True):
numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
start_mem = df.memory_usage().sum() / 1024**2
for col in df.columns:
col_type = df[col].dtypes
if col_type in numerics:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int8)
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int16)
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
else:
if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
df[col] = df[col].astype(np.float16)
elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
df[col] = df[col].astype(np.float32)
else:
df[col] = df[col].astype(np.float64)
end_mem = df.memory_usage().sum() / 1024**2
if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))
return df
def grab_col_names(dataframe, cat_th=61, car_th=74):
"""
It gives the names of categorical, numerical and categorical but cardinal variables in the data set.
Note: Categorical variables with numerical appearance are also included in categorical variables.
Parameters
------
dataframe: dataframe
The dataframe from which variable names are to be retrieved
cat_th: int, optional
Class threshold value for numeric but categorical variables
car_th: int, optinal
Class threshold for categorical but cardinal variables
Returns
------
cat_cols: list
Categorical variable list
num_cols: list
Numerical variable list
cat_but_car: list
Categorical view cardinal variable list
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = the total number of variables
num_but_cat is inside cat_cols.
The sum of 3 lists with return is equal to the total number of variables: cat_cols + num_cols + cat_but_car = number of variables
"""
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == 'O']
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes != 'O']
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and dataframe[col].dtypes == 'O']
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != 'O']
num_cols = [col for col in num_cols if col not in num_but_cat]
return (cat_cols, num_cols, cat_but_car)
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
if na_name:
return (na_columns, missing_df)
for i in train_df.columns:
if train_df[i].dtypes == 'int64' or train_df[i].dtypes == 'float64':
train_df[i].fillna(train_df[i].mean(), inplace=True)
for i in test_df.columns:
if test_df[i].dtypes == 'int64' or test_df[i].dtypes == 'float64':
test_df[i].fillna(test_df[i].mean(), inplace=True)
for i in train_df.columns:
if train_df[i].dtypes == 'object':
train_df[i].fillna(train_df[i].mode()[0], inplace=True)
for i in test_df.columns:
if test_df[i].dtypes == 'object':
test_df[i].fillna(test_df[i].mode()[0], inplace=True)
train_df.isnull().sum()
num_cols2 = num_cols
num_cols2.remove('TransactionID')
train_df = pd.get_dummies(train_df[cat_cols + num_cols2], drop_first=True)
cat_cols.remove('isFraud')
test_df = pd.get_dummies(test_df[cat_cols + num_cols2], drop_first=True)
test_df.shape | code |
89138263/cell_46 | [
"text_plain_output_1.png"
] | 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)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
train_df = train_df.rename(columns=lambda x: ''.join(x.split('_')))
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
test_df = test_df.rename(columns=lambda x: ''.join(x.split('_')))
def reduce_mem_usage(df, verbose=True):
numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
start_mem = df.memory_usage().sum() / 1024**2
for col in df.columns:
col_type = df[col].dtypes
if col_type in numerics:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int8)
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int16)
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
else:
if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
df[col] = df[col].astype(np.float16)
elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
df[col] = df[col].astype(np.float32)
else:
df[col] = df[col].astype(np.float64)
end_mem = df.memory_usage().sum() / 1024**2
if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))
return df
def grab_col_names(dataframe, cat_th=61, car_th=74):
"""
It gives the names of categorical, numerical and categorical but cardinal variables in the data set.
Note: Categorical variables with numerical appearance are also included in categorical variables.
Parameters
------
dataframe: dataframe
The dataframe from which variable names are to be retrieved
cat_th: int, optional
Class threshold value for numeric but categorical variables
car_th: int, optinal
Class threshold for categorical but cardinal variables
Returns
------
cat_cols: list
Categorical variable list
num_cols: list
Numerical variable list
cat_but_car: list
Categorical view cardinal variable list
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = the total number of variables
num_but_cat is inside cat_cols.
The sum of 3 lists with return is equal to the total number of variables: cat_cols + num_cols + cat_but_car = number of variables
"""
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == 'O']
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes != 'O']
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and dataframe[col].dtypes == 'O']
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != 'O']
num_cols = [col for col in num_cols if col not in num_but_cat]
return (cat_cols, num_cols, cat_but_car)
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
if na_name:
return (na_columns, missing_df)
for i in train_df.columns:
if train_df[i].dtypes == 'int64' or train_df[i].dtypes == 'float64':
train_df[i].fillna(train_df[i].mean(), inplace=True)
for i in train_df.columns:
if train_df[i].dtypes == 'object':
train_df[i].fillna(train_df[i].mode()[0], inplace=True)
train_df.isnull().sum()
num_cols2 = num_cols
num_cols2.remove('TransactionID')
train_df = pd.get_dummies(train_df[cat_cols + num_cols2], drop_first=True)
train_df.shape | code |
89138263/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
train_df = train_df.rename(columns=lambda x: ''.join(x.split('_')))
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
test_df = test_df.rename(columns=lambda x: ''.join(x.split('_')))
def grab_col_names(dataframe, cat_th=61, car_th=74):
"""
It gives the names of categorical, numerical and categorical but cardinal variables in the data set.
Note: Categorical variables with numerical appearance are also included in categorical variables.
Parameters
------
dataframe: dataframe
The dataframe from which variable names are to be retrieved
cat_th: int, optional
Class threshold value for numeric but categorical variables
car_th: int, optinal
Class threshold for categorical but cardinal variables
Returns
------
cat_cols: list
Categorical variable list
num_cols: list
Numerical variable list
cat_but_car: list
Categorical view cardinal variable list
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = the total number of variables
num_but_cat is inside cat_cols.
The sum of 3 lists with return is equal to the total number of variables: cat_cols + num_cols + cat_but_car = number of variables
"""
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == 'O']
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes != 'O']
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and dataframe[col].dtypes == 'O']
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != 'O']
num_cols = [col for col in num_cols if col not in num_but_cat]
return (cat_cols, num_cols, cat_but_car)
cat_cols, num_cols, cat_but_car = grab_col_names(train_df) | code |
2033671/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
home_sale_price = data.SalePrice
column_interest = ['BsmtFinSF1', 'SalePrice']
two_column_data = data[column_interest]
y = data.SalePrice
Cost_predictors = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = data[Cost_predictors]
from sklearn.tree import DecisionTreeRegressor
price_model = DecisionTreeRegressor()
price_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
predicted_home_price = price_model.predict(X)
mean_absolute_error(y, predicted_home_price) | code |
2033671/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
for max_leaf_nodes in [5, 50, 500, 5000]:
my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)
print('max_leaf_nodes: %d \t\t Mean_Absolute_error: %d' % (max_leaf_nodes, my_mae)) | code |
2033671/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
print(data.columns)
home_sale_price = data.SalePrice
print(home_sale_price.head())
column_interest = ['BsmtFinSF1', 'SalePrice']
two_column_data = data[column_interest]
two_column_data.describe() | code |
2033671/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor()
forest_model.fit(train_X, train_y)
price_preds = forest_model.predict(val_X)
print(mean_absolute_error(val_y, price_preds)) | code |
2033671/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
home_sale_price = data.SalePrice
column_interest = ['BsmtFinSF1', 'SalePrice']
two_column_data = data[column_interest]
y = data.SalePrice
Cost_predictors = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = data[Cost_predictors]
from sklearn.tree import DecisionTreeRegressor
price_model = DecisionTreeRegressor()
price_model.fit(X, y)
print('making prediction for following houses')
print(X.head())
print('The predictions are: ')
print(price_model.predict(X.head())) | code |
2033671/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
home_sale_price = data.SalePrice
column_interest = ['BsmtFinSF1', 'SalePrice']
two_column_data = data[column_interest]
y = data.SalePrice
Cost_predictors = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = data[Cost_predictors]
from sklearn.tree import DecisionTreeRegressor
price_model = DecisionTreeRegressor()
price_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
predicted_home_price = price_model.predict(X)
mean_absolute_error(y, predicted_home_price)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=0)
price_model.fit(train_X, train_y)
val_predictions = price_model.predict(val_X)
print(mean_absolute_error(val_y, val_predictions)) | code |
16134883/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
print('Showing Meta Data :')
data.info() | code |
16134883/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
def categorical_data(i):
dataset[i].value_counts().plot(kind='bar')
j_1 = ['Channel', 'Region']
for k in j_1:
categorical_data(i=k)
plt.show() | code |
16134883/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape | code |
16134883/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
list(log_data.columns) | code |
16134883/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
sns.set()
j = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']
for k in j:
continous_data(i=k)
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
def categorical_data(i):
pass
j_1 = ['Channel', 'Region']
for k in j_1:
categorical_data(i=k)
dataset.corr()
def scatterplot(i, j):
pass
scatterplot(i='Milk', j='Detergents_Paper') | code |
16134883/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
sns.set()
j = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']
for k in j:
continous_data(i=k) | code |
16134883/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
type(data) | code |
16134883/cell_29 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
sns.set()
j = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']
for k in j:
continous_data(i=k)
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
def categorical_data(i):
pass
j_1 = ['Channel', 'Region']
for k in j_1:
categorical_data(i=k)
dataset.corr()
def scatterplot(i, j):
pass
scatterplot(i='Milk', j='Grocery') | code |
16134883/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
dataset.corr() | code |
16134883/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts() | code |
16134883/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
dataset.head() | code |
16134883/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
pd.isnull(data).sum()
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
sns.set()
j = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']
for k in j:
continous_data(i=k)
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
def categorical_data(i):
pass
j_1 = ['Channel', 'Region']
for k in j_1:
categorical_data(i=k)
dataset.corr()
def scatterplot(i, j):
pass
def categorical_multi(i, j):
pd.crosstab(dataset[i], dataset[j]).plot(kind='bar')
plt.show()
print(pd.crosstab(dataset[i], dataset[j]))
categorical_multi(i='Channel', j='Region') | code |
16134883/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
print('Descriptive Statastics of our Data:')
data.describe().T | code |
16134883/cell_38 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
list(log_data.columns)
for k in list(log_data.columns):
IQR = np.percentile(log_data[k], 75) - np.percentile(log_data[k], 25)
Outlier_top = np.percentile(log_data[k], 75) + 1.5 * IQR
Outlier_bottom = np.percentile(log_data[k], 25) - 1.5 * IQR
log_data[k] = np.where(log_data[k] > Outlier_top, Outlier_top, log_data[k])
log_data[k] = np.where(log_data[k] < Outlier_bottom, Outlier_bottom, log_data[k])
dataset1 = log_data.copy()
list(dataset1.columns) | code |
16134883/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16134883/cell_31 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
sns.set()
j = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']
for k in j:
continous_data(i=k)
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
def categorical_data(i):
pass
j_1 = ['Channel', 'Region']
for k in j_1:
categorical_data(i=k)
dataset.corr()
def scatterplot(i, j):
pass
scatterplot(i='Detergents_Paper', j='Grocery') | code |
16134883/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
log_data.head() | code |
16134883/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
dataset.head() | code |
16134883/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
pd.isnull(data).sum() | code |
16134883/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
sns.set()
j = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']
for k in j:
continous_data(i=k)
def categorical_data(i):
pass
j_1 = ['Channel', 'Region']
for k in j_1:
categorical_data(i=k)
dataset.corr()
print('Correlation Heat map of the data')
plt.figure(figsize=(10, 6))
sns.heatmap(dataset.corr(), annot=True, fmt='.2f', vmin=-1, vmax=1, cmap='Spectral')
plt.show() | code |
16134883/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
pd.isnull(data).sum()
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
sns.set()
j = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']
for k in j:
continous_data(i=k)
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
def categorical_data(i):
pass
j_1 = ['Channel', 'Region']
for k in j_1:
categorical_data(i=k)
dataset.corr()
def scatterplot(i, j):
pass
def categorical_multi(i, j):
pass
categorical_multi(i='Channel', j='Region')
list(log_data.columns)
for k in list(log_data.columns):
IQR = np.percentile(log_data[k], 75) - np.percentile(log_data[k], 25)
Outlier_top = np.percentile(log_data[k], 75) + 1.5 * IQR
Outlier_bottom = np.percentile(log_data[k], 25) - 1.5 * IQR
log_data[k] = np.where(log_data[k] > Outlier_top, Outlier_top, log_data[k])
log_data[k] = np.where(log_data[k] < Outlier_bottom, Outlier_bottom, log_data[k])
def continous_data(i):
if log_data[i].dtype != 'object':
log_data[i].plot.kde()
plt.clf()
for k in j:
continous_data(i=k)
sns.pairplot(log_data, diag_kind='kde') | code |
16134883/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
data.Region.value_counts()
data.Channel.value_counts() | code |
16134883/cell_5 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"text_plain_output_2.png",
"text_plain_output_1.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
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.head() | code |
16134883/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/Wholesale customers data.csv')
data.shape
data.describe().T
pd.isnull(data).sum()
data.Region.value_counts()
data.Channel.value_counts()
dataset = data.copy()
def continous_data(i):
if dataset[i].dtype != 'object':
plt.clf()
sns.set()
j = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']
for k in j:
continous_data(i=k)
log_data = np.log(dataset[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']].copy())
def categorical_data(i):
pass
j_1 = ['Channel', 'Region']
for k in j_1:
categorical_data(i=k)
dataset.corr()
def scatterplot(i, j):
pass
def categorical_multi(i, j):
pass
categorical_multi(i='Channel', j='Region')
list(log_data.columns)
for k in list(log_data.columns):
IQR = np.percentile(log_data[k], 75) - np.percentile(log_data[k], 25)
Outlier_top = np.percentile(log_data[k], 75) + 1.5 * IQR
Outlier_bottom = np.percentile(log_data[k], 25) - 1.5 * IQR
log_data[k] = np.where(log_data[k] > Outlier_top, Outlier_top, log_data[k])
log_data[k] = np.where(log_data[k] < Outlier_bottom, Outlier_bottom, log_data[k])
def continous_data(i):
if log_data[i].dtype != 'object':
print('--' * 60)
sns.boxplot(log_data[i])
plt.title('Boxplot of ' + str(i))
plt.show()
plt.title('histogram of ' + str(i))
log_data[i].plot.kde()
plt.show()
plt.clf()
for k in j:
continous_data(i=k) | code |
2011240/cell_42 | [
"text_plain_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='Pclass', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
deck = titanic['Cabin'].dropna()
levels = []
for level in deck:
levels.append(level[0])
cabin = DataFrame(levels)
cabin.columns = ['Cabin']
cabin = cabin[cabin.Cabin != 'T']
titanic['Alone'] = titanic.SibSp + titanic.Parch
sns.factorplot('Alone', data=titanic, kind='count', palette='hot') | code |
2011240/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='Pclass', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend() | code |
2011240/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
def split(passenger):
age, sex = passenger
if age < 16:
return 'child'
else:
return sex
titanic['person'] = titanic[['Age', 'Sex']].apply(split, axis=1)
titanic[0:10] | code |
2011240/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
sns.factorplot('Sex', data=titanic, hue='Pclass', kind='count') | code |
2011240/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
deck = titanic['Cabin'].dropna()
deck.head() | code |
2011240/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.info() | code |
2011240/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='Pclass', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
deck = titanic['Cabin'].dropna()
levels = []
for level in deck:
levels.append(level[0])
cabin = DataFrame(levels)
cabin.columns = ['Cabin']
cabin = cabin[cabin.Cabin != 'T']
sns.factorplot('Embarked', hue='Pclass', data=titanic, kind='count', palette='ocean') | code |
2011240/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.head() | code |
2011240/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='Pclass', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
deck = titanic['Cabin'].dropna()
levels = []
for level in deck:
levels.append(level[0])
cabin = DataFrame(levels)
cabin.columns = ['Cabin']
cabin = cabin[cabin.Cabin != 'T']
sns.factorplot('Cabin', data=cabin, palette='rainbow', kind='count') | code |
2011240/cell_33 | [
"text_plain_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='Pclass', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
deck = titanic['Cabin'].dropna()
levels = []
for level in deck:
levels.append(level[0])
cabin = DataFrame(levels)
cabin.columns = ['Cabin']
cabin = cabin[cabin.Cabin != 'T']
sns.factorplot('Embarked', data=titanic, kind='count', color='blue') | code |
2011240/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic['Survivor'] = titanic.Survived.map({0: 'No', 1: 'Yes'})
titanic.head() | code |
2011240/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend() | code |
2011240/cell_40 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic['Alone'].loc[titanic['Alone'] > 0] = 'With Family'
titanic['Alone'].loc[titanic['Alone'] == 0] = 'Alone' | code |
2011240/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic['Alone'] | code |
2011240/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic.head() | code |
2011240/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend() | code |
2011240/cell_45 | [
"text_html_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='Pclass', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
deck = titanic['Cabin'].dropna()
levels = []
for level in deck:
levels.append(level[0])
cabin = DataFrame(levels)
cabin.columns = ['Cabin']
cabin = cabin[cabin.Cabin != 'T']
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic['Survivor'] = titanic.Survived.map({0: 'No', 1: 'Yes'})
sns.factorplot('Survivor', data=titanic, kind='count') | code |
2011240/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['person'].value_counts() | code |
2011240/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.head() | code |
2011240/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
sns.factorplot('Sex', data=titanic, kind='count', color='red') | code |
2011240/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Age'].hist(bins=70, color='blue') | code |
2011240/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
titanic = pd.read_csv('../input/train.csv')
plt.hist(titanic['Age'].dropna(), bins=70) | code |
2011240/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Alone'] = titanic.SibSp + titanic.Parch
titanic.head() | code |
2011240/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.head() | code |
2011240/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic['Age'].mean() | code |
2011240/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
sns.factorplot('Pclass', hue='person', data=titanic, kind='count') | code |
2011240/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
sns.factorplot('Pclass', data=titanic, hue='Sex', kind='count') | code |
2011240/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import Series, DataFrame
import pandas as pd
import seaborn as sns
titanic = pd.read_csv('../input/train.csv')
fig = sns.FacetGrid(titanic, hue='Sex', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='person', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
fig = sns.FacetGrid(titanic, hue='Pclass', aspect=4)
fig.map(sns.kdeplot, 'Age', shade=True)
oldest = titanic['Age'].max()
fig.set(xlim=(0, oldest))
fig.add_legend()
deck = titanic['Cabin'].dropna()
levels = []
for level in deck:
levels.append(level[0])
cabin = DataFrame(levels)
cabin.columns = ['Cabin']
sns.factorplot('Cabin', data=cabin, palette='winter_d', kind='count') | code |
2011240/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.describe() | code |
2011240/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
titanic = pd.read_csv('../input/train.csv')
titanic.head() | code |
128024882/cell_13 | [
"text_plain_output_1.png"
] | from statsmodels.sandbox.regression import gmm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
instrument_df = data[['cons_lagged1', 'interest_lagged_1', 'lagged_cons_growth']]
endog_df = data[['int_forward', 'cons_forward', 'PCE']]
endog, instrument = map(np.asarray, [endog_df, instrument_df])
def moment_consumption1(params, endog):
beta, gamma = params
r_forw1, c_forw1, c = endog.T
err = beta * (1 + r_forw1) * np.power(c_forw1 / c, -gamma) - 1
return err
dependant = np.ones(endog.shape[0])
mod1 = gmm.NonlinearIVGMM(dependant, endog, instrument, moment_consumption1, k_moms=4)
I = np.eye(3)
res_two_way = mod1.fit([1, -1], maxiter=2, inv_weights=I)
res_iterated = mod1.fit([1, -1], maxiter=100, inv_weights=I, weights_method='hac', wargs={'maxlag': 4})
start = (0, 0)
CUE_results = mod1.fitgmm_cu(start, optim_method='bfgs', optim_args=None)
print(CUE_results) | code |
128024882/cell_9 | [
"text_plain_output_1.png"
] | from statsmodels.sandbox.regression import gmm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
instrument_df = data[['cons_lagged1', 'interest_lagged_1', 'lagged_cons_growth']]
endog_df = data[['int_forward', 'cons_forward', 'PCE']]
endog, instrument = map(np.asarray, [endog_df, instrument_df])
def moment_consumption1(params, endog):
beta, gamma = params
r_forw1, c_forw1, c = endog.T
err = beta * (1 + r_forw1) * np.power(c_forw1 / c, -gamma) - 1
return err
dependant = np.ones(endog.shape[0])
mod1 = gmm.NonlinearIVGMM(dependant, endog, instrument, moment_consumption1, k_moms=4)
I = np.eye(3)
res_two_way = mod1.fit([1, -1], maxiter=2, inv_weights=I) | code |
128024882/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
data.head() | code |
128024882/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 |
128024882/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
data.columns | code |
128024882/cell_10 | [
"text_html_output_1.png"
] | from statsmodels.sandbox.regression import gmm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
instrument_df = data[['cons_lagged1', 'interest_lagged_1', 'lagged_cons_growth']]
endog_df = data[['int_forward', 'cons_forward', 'PCE']]
endog, instrument = map(np.asarray, [endog_df, instrument_df])
def moment_consumption1(params, endog):
beta, gamma = params
r_forw1, c_forw1, c = endog.T
err = beta * (1 + r_forw1) * np.power(c_forw1 / c, -gamma) - 1
return err
dependant = np.ones(endog.shape[0])
mod1 = gmm.NonlinearIVGMM(dependant, endog, instrument, moment_consumption1, k_moms=4)
I = np.eye(3)
res_two_way = mod1.fit([1, -1], maxiter=2, inv_weights=I)
print(res_two_way.summary(yname='Euler Eq', xname=['discount', 'CRRA'])) | code |
128024882/cell_12 | [
"text_plain_output_1.png"
] | from statsmodels.sandbox.regression import gmm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/comp-macro/data.csv')
data = data.dropna()
instrument_df = data[['cons_lagged1', 'interest_lagged_1', 'lagged_cons_growth']]
endog_df = data[['int_forward', 'cons_forward', 'PCE']]
endog, instrument = map(np.asarray, [endog_df, instrument_df])
def moment_consumption1(params, endog):
beta, gamma = params
r_forw1, c_forw1, c = endog.T
err = beta * (1 + r_forw1) * np.power(c_forw1 / c, -gamma) - 1
return err
dependant = np.ones(endog.shape[0])
mod1 = gmm.NonlinearIVGMM(dependant, endog, instrument, moment_consumption1, k_moms=4)
I = np.eye(3)
res_two_way = mod1.fit([1, -1], maxiter=2, inv_weights=I)
res_iterated = mod1.fit([1, -1], maxiter=100, inv_weights=I, weights_method='hac', wargs={'maxlag': 4})
print('\n\n')
print(res_iterated.summary(yname='Euler Eq', xname=['discount', 'CRRA'])) | code |
74061207/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
df.isna().sum()
plot_1=sns.histplot(data=df, x='Ship_Mode')
plt.show()
plot_2=sns.histplot(data=df, x='Order_Priority')
plt.show()
plot_3=sns.histplot(data=df, x='Customer_Segment')
plt.show()
plot_4=sns.histplot(data=df, x='Product_Category')
plt.show()
plot_5=sns.histplot(data=df, x='Product_Container')
plt.show()
plot_6=sns.barplot(data=df,x='Order_Priority',y='Profit',hue='Ship_Mode')
plt.show()
plot_7=sns.barplot(data=df,x='Region',y='Profit',hue='Ship_Mode')
plt.xticks(rotation=45)
plt.show()
plot_8=sns.barplot(data=df,x='Region',y='Sales',hue='Ship_Mode')
plt.xticks(rotation=45)
plt.show()
plot_9=sns.barplot(data=df,x='Region',y='Profit',hue='Customer_Segment')
plt.xticks(rotation=45)
plt.show()
plot_10=sns.barplot(data=df,x='Region',y='Profit',hue='Product_Category')
plt.xticks(rotation=45)
plt.show()
plot_11=sns.lineplot(data=df,x='Order_Quantity',y='Sales')
plt.show()
plot_12=sns.lmplot(data=df,x='Order_Quantity',y='Profit')
plt.show()
plot_14=sns.barplot(data=df,x='Product_Category',y='Profit',hue='Product_Container')
plt.show()
plot_11 = sns.regplot(data=df, x='Sales', y='Profit')
plt.show() | code |
74061207/cell_4 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/sales-store-product-details/Salesstore.csv')
df.isna().sum() | code |
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