Select DataFrame

Easy Select Column Method from Pandas DataFrame

tools.select.get_batch_dataframe(df, batch_size=100)

split DataFrame to sub-DataDrame and each sub-DataDrame row size is batch_size

Parameters:

• df (Pandas DataFrame) –
• batch_size (number of records in each sub-dataframe(default: 100)) –

Returns:

Return type:

DataFrame generator

Examples

>>> import pandas as pd
>>> from tidyframe import get_batch_dataframe
>>> df = pd.DataFrame()
>>> df['col_1'] = list("abcde")
>>> df['col_2'] = [1, 2, 3, 4, 5]
>>> dfs = [ x for x in get_batch_dataframe(df,2)]
>>> dfs[-1]
    col_1  col_2
4       e      5
>>> [ x.shape[0] for x in dfs]
[2, 2, 1]

tools.select.reorder_columns(df, columns=None, pattern=None, last_columns=None)

reorder columns of pandas DataFrame

Parameters:

• df (Pandas DataFrame) –
• columns (list which want to head column name(non-use if pattern is not None)) –
• pattern (regular expression pattern which let selected columns be at head columns) –
• last_columns (list which want to last column name) –

Returns:

Return type:

Pandas DataFrame

Examples

>>> import pandas as pd
>>> from tidyframe import reorder_columns
>>> df = pd.DataFrame([{'a': 1, 'b': 1, 'c': 1, 'd': 1, 'e': 2}])
>>> df_reorder = reorder_columns(df, ['b', 'c'], last_columns=['a', 'd'])
>>> df_reorder
b  c  e  a  d
0  1  1  2  1  1

tools.select.select(df, columns=None, columns_minus=None, columns_between=None, pattern=None, copy=False)

Select Pandas DataFrame Columns

Parameters:

• df (Pandas DataFrame) –
• columns_minus (column which want to remove) –
• columns_between (list with two element, select columns bwtween two columns) –
• pattern (regular expression or list of regular expression, return match columns) –
• copy (whether return deep copy DataFrame) –

Returns:

Return type:

Pandas DataFrame

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from tidyframe import select
>>> df = pd.DataFrame(np.array(range(10)).reshape(2, 5),
...                   columns=list('abcde'),
...                   index=['row_1', 'row_2'])
>>> select(df, columns=['b', 'd'])
    b  d
row_1  1  3
row_2  6  8
>>> select(df, columns_minus=['b', 'd'])
    a  c  e
row_1  0  2  4
row_2  5  7  9
>>> select(df, pattern='[a|b]')
    a  b
row_1  0  1
row_2  5  6

tools.select.select_index(x, i, otherwise=nan)

Select by index and Catch all Exception

Parameters:

• x (array) –
• i (index) –
• otherwise (fill value if exist exception) –

Returns:

Return type:

x[i] if not exception happen else return otherwise