guerbai

## select_sub_dataframe.py
df = reviews.loc[[0, 1, 10, 100], ['country', 'province', 'region_1', 'region_2']]

## remove_categorical_cols.py
drop_X_train = X_train.select_dtypes(exclude=['object'])

# or

num_X_train = X_train.drop(object_cols, axis=1)

## get_all_categorical_variables.py
s = (X_train.dtypes == 'object')
object_cols = list(s[s].index)

print("Categorical variables:")
print(object_cols)

## get_nan_num_by_col.py
missing_val_count_by_column = (df.isnull().sum())
print (missing_val_count_by_column[missing_val_count_by_column > 0])

## remove_nan_col.py
cols_with_missing = [col for col in X_train.columns
                     if df[col].isnull().any()]
reduced_df = df.drop(cols_with_missing, axis=1)

## init_pandas.py
df = pd.DataFrame(np.arange(12).reshape(3,4),
                      columns=['A', 'B', 'C', 'D'])

## dataframe_tail.py
df.tail()

## figure_size.py
plt.figure(figsize=(16,6))

## dataframe_time_index.py
fifa_data = pd.read_csv(fifa_filepath, index_col="Date", parse_dates=True)

## ignore_warning.py
import warnings # current version of seaborn generates a bunch of warnings that we'll ignore
warnings.filterwarnings("ignore")
	drop_X_train = X_train.select_dtypes(exclude=['object'])

	# or

	num_X_train = X_train.drop(object_cols, axis=1)
	s = (X_train.dtypes == 'object')
	object_cols = list(s[s].index)

	print("Categorical variables:")
	print(object_cols)
	missing_val_count_by_column = (df.isnull().sum())
	print (missing_val_count_by_column[missing_val_count_by_column > 0])
	cols_with_missing = [col for col in X_train.columns
	if df[col].isnull().any()]
	reduced_df = df.drop(cols_with_missing, axis=1)
	df = pd.DataFrame(np.arange(12).reshape(3,4),
	columns=['A', 'B', 'C', 'D'])
	import warnings # current version of seaborn generates a bunch of warnings that we'll ignore
	warnings.filterwarnings("ignore")