(‘SMS’). These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. (GH1543) 1.1. v0.19.0 (October 2, 2016) 11 pandas: powerful Python data analysis respectively. If more than one sheet is specified, a dictionary is returned. (GH9450) # Returns the 1st and 4th sheet, as a dictionary of DataFrames. pd.read_excel('path_to_file.xls',sheetname=['Sheet1' 1 1 6 2 2 7 3 3 8 4 4 9 In [12]: gr = df.groupby(df['jim'] < 2) previous behavior (excludes 1st column from output): In [4]: gr.apply(sum) Out[4]: joe jim False 24 True 11 current behavior:

(‘SMS’). These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. (GH1543) In [44]: from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin respectively. If more than one sheet is specified, a dictionary is returned. (GH9450) # Returns the 1st and 4th sheet, as a dictionary of DataFrames. pd.read_excel('path_to_file.xls',sheetname=['Sheet1' 1 1 6 2 2 7 3 3 8 4 4 9 In [12]: gr = df.groupby(df['jim'] < 2) previous behavior (excludes 1st column from output): In [4]: gr.apply(sum) Out[4]: joe jim False 24 True 11 current behavior:

(‘SMS’). These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. (GH1543) In [44]: from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin respectively. If more than one sheet is specified, a dictionary is returned. (GH9450) # Returns the 1st and 4th sheet, as a dictionary of DataFrames. pd.read_excel('path_to_file.xls',sheetname=['Sheet1' 1 1 6 2 2 7 3 3 8 4 4 9 In [12]: gr = df.groupby(df['jim'] < 2) previous behavior (excludes 1st column from output): 208 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release

(‘SMS’). These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. (GH1543) In [44]: from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin respectively. If more than one sheet is specified, a dictionary is returned. (GH9450) # Returns the 1st and 4th sheet, as a dictionary of DataFrames. pd.read_excel('path_to_file.xls',sheetname=['Sheet1' 1 1 6 2 2 7 3 3 8 4 4 9 In [12]: gr = df.groupby(df['jim'] < 2) previous behavior (excludes 1st column from output): In [4]: gr.apply(sum) Out[4]: joe jim False 24 True 11 current behavior:

(‘SMS’). These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively. (GH1543) In [44]: from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin respectively. If more than one sheet is specified, a dictionary is returned. (GH9450) # Returns the 1st and 4th sheet, as a dictionary of DataFrames. pd.read_excel('path_to_file.xls',sheetname=['Sheet1' 1 1 6 2 2 7 3 3 8 4 4 9 In [12]: gr = df.groupby(df['jim'] < 2) previous behavior (excludes 1st column from output): 1.17. v0.15.1 (November 9, 2014) 237 pandas: powerful Python data analysis toolkit

respectively. If more than one sheet is specified, a dictionary is returned. (GH9450) # Returns the 1st and 4th sheet, as a dictionary of DataFrames. pd.read_excel('path_to_file.xls',sheetname=['Sheet1' 1 1 6 2 2 7 3 3 8 4 4 9 In [12]: gr = df.groupby(df['jim'] < 2) previous behavior (excludes 1st column from output): In [4]: gr.apply(sum) Out[4]: joe jim False 24 True 11 current behavior: In Out[69]: MyData AA one 11 six 22 BB one 33 two 44 six 55 To take the cross section of the 1st level and 1st axis the index: In [70]: df.xs('BB',level=0,axis=0) #Note : level and axis are optional, and

tools for performing the above tasks. Create a range of dates: # 72 hours starting with midnight Jan 1st, 2011 In [1]: rng = date_range(’1/1/2011’, periods=72, freq=’H’) In [2]: rng[:5] Out[2]: st. With dayfirst=False (default) it will guess “01/12/2011” to be January 12th. # Try to infer the format gov/bank/individual/failed/banklist.html’ In [193]: dfs = read_html(url) In [194]: dfs Out[194]: [ Bank Name City ST \ 0 Syringa Bank Boise ID 1 The Bank of Union El Reno OK 2 DuPage National Bank West Chicago IL

Plan for dropping Python 2.7 The Python core team plans to stop supporting Python 2.7 on January 1st, 2020. In line with NumPy’s plans, all pandas releases through December 31, 2018 will support Python infer_datetime_format is sensitive to dayfirst. With dayfirst=True, it will guess “01/12/2011” to be December 1st. With dayfirst=False (default) it will guess “01/12/2011” to be January 12th. # Try to infer the format control over which level to end normalization. With max_level=1 the follow- ing snippet normalizes until 1st nesting level of the provided dict. In [258]: data = [{'CreatedBy': {'Name': 'User001'}, .....: 'Lookup':

infer_datetime_format is sensitive to dayfirst. With dayfirst=True, it will guess “01/12/2011” to be December 1st. With dayfirst=False (default) it will guess “01/12/2011” to be January 12th. # Try to infer the format control over which level to end normalization. With max_level=1 the follow- ing snippet normalizes until 1st nesting level of the provided dict. In [258]: data = [{'CreatedBy': {'Name': 'User001'}, .....: 'Lookup': failed/banklist.html' In [295]: dfs = pd.read_html(url) In [296]: dfs Out[296]: [ Bank Name City ST CERT ˓→ Acquiring Institution Closing Date Updated Date 0 The Enloe State Bank Cooper TX 10716 ˓→

infer_datetime_format is sensitive to dayfirst. With dayfirst=True, it will guess 01/12/2011 to be December 1st. With dayfirst=False (default) it will guess 01/12/2011 to be January 12th. # Try to infer the format control over which level to end normalization. With max_level=1 the follow- ing snippet normalizes until 1st nesting level of the provided dict. In [258]: data = [{'CreatedBy': {'Name': 'User001'}, .....: 'Lookup': read_excel('path_to_file.xls', sheet_name=None) Using a list to get multiple sheets: # Returns the 1st and 4th sheet, as a dictionary of DataFrames. pd.read_excel('path_to_file.xls', sheet_name=['Sheet1'