libraries like etree and lxml to build the necessary document and not by string concatenation or regex adjustments. Always remember XML is a special text file with markup rules. • With very large XML files (several set a new column color to ‘green’ when the second column has ‘Z’. You can do the following: In [208]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')}) In [209]: df['color'] = np.where(df['col2'] where(df['col2'] == 'Z', 'green', 'red') In [210]: df Out[210]: col1 col2 color 0 A Z green 1 B Z green 2 B X red 3 C Y red If you have multiple conditions, you can use numpy.select() to achieve that.

libraries like etree and lxml to build the necessary document and not by string concatenation or regex adjustments. Always remember XML is a special text file with markup rules. • With very large XML files (several set a new column color to ‘green’ when the second column has ‘Z’. You can do the following: In [208]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')}) In [209]: df['color'] = np.where(df['col2'] where(df['col2'] == 'Z', 'green', 'red') In [210]: df Out[210]: col1 col2 color 0 A Z green 1 B Z green 2 B X red 3 C Y red If you have multiple conditions, you can use numpy.select() to achieve that.

libraries like etree and lxml to build the necessary document and not by string concatenation or regex adjustments. Always remember XML is a special text file with markup rules. • With very large XML files (several set a new column color to ‘green’ when the second column has ‘Z’. You can do the following: In [208]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')}) In [209]: df['color'] = np.where(df['col2'] where(df['col2'] == 'Z', 'green', 'red') In [210]: df Out[210]: col1 col2 color 0 A Z green 1 B Z green 2 B X red 3 C Y red If you have multiple conditions, you can use numpy.select() to achieve that.

libraries like etree and lxml to build the necessary document and not by string concatenation or regex adjustments. Always remember XML is a special text file with markup rules. • With very large XML files (several set a new column color to ‘green’ when the second column has ‘Z’. You can do the following: In [210]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')}) In [211]: df['color'] = np.where(df['col2'] where(df['col2'] == 'Z', 'green', 'red') In [212]: df Out[212]: col1 col2 color 0 A Z green 1 B Z green 2 B X red 3 C Y red If you have multiple conditions, you can use numpy.select() to achieve that.

libraries like etree and lxml to build the necessary document and not by string concatenation or regex adjustments. Always remember XML is a special text file with markup rules. • With very large XML files (several set a new column color to ‘green’ when the second column has ‘Z’. You can do the following: In [210]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')}) In [211]: df['color'] = np.where(df['col2'] pandas: powerful Python data analysis toolkit, Release 1.4.4 (continued from previous page) col1 col2 color 0 A Z green 1 B Z green 2 B X red 3 C Y red If you have multiple conditions, you can use numpy

libraries like etree and lxml to build the necessary document and not by string concatenation or regex adjustments. Always remember XML is a special text file with markup rules. • With very large XML files (several set a new column color to ‘green’ when the second column has ‘Z’. You can do the following: In [210]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')}) In [211]: df['color'] = np.where(df['col2'] where(df['col2'] == 'Z', 'green', 'red') In [212]: df Out[212]: col1 col2 color 0 A Z green 1 B Z green 2 B X red 3 C Y red If you have multiple conditions, you can use numpy.select() to achieve that.

MultiIndex.from_arrays([colors, foods], names=['color', 'food']) In [220]: df = pd.DataFrame(np.random.randn(n, 2), index=index) In [221]: df Out[221]: 0 1 color food red ham 0.194889 -0.381994 ham 0.318587 095031 -0.270099 eggs -0.707140 -0.773882 eggs 0.229453 0.304418 In [222]: df.query('color == "red"') Out[222]: 0 1 color food red ham 0.194889 -0.381994 ham 0.318587 2.089075 eggs -0.728293 -0.090255 ----> 1 df.iloc[5].plot.bar() NameError: name 'df' is not defined In [19]: plt.axhline(0, color='k'); 2.14. Visualization 593 pandas: powerful Python data analysis toolkit, Release 1.1.1 Calling

MultiIndex.from_arrays([colors, foods], names=['color', 'food']) In [220]: df = pd.DataFrame(np.random.randn(n, 2), index=index) In [221]: df Out[221]: 0 1 color food red ham 0.194889 -0.381994 ham 0.318587 095031 -0.270099 eggs -0.707140 -0.773882 eggs 0.229453 0.304418 In [222]: df.query('color == "red"') Out[222]: 0 1 color food red ham 0.194889 -0.381994 ham 0.318587 2.089075 eggs -0.728293 -0.090255 ----> 1 df.iloc[5].plot.bar() NameError: name 'df' is not defined In [19]: plt.axhline(0, color='k'); 2.14. Visualization 593 pandas: powerful Python data analysis toolkit, Release 1.1.0 Calling

set a new column color to ‘green’ when the second column has ‘Z’. You can do the following: In [208]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')}) In [209]: df['color'] = np.where(df['col2'] where(df['col2'] == 'Z', 'green', 'red') In [210]: df Out[210]: col1 col2 color 0 A Z green 1 B Z green 2 B X red 3 C Y red If you have multiple conditions, you can use numpy.select() to achieve that. Say corresponding to three condi- tions there are three choice of colors, with a fourth color as a fallback, you can do the following. In [211]: conditions = [ .....: (df['col2'] == 'Z') & (df['col1']

set a new column color to ‘green’ when the second column has ‘Z’. You can do the following: In [208]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')}) In [209]: df['color'] = np.where(df['col2'] where(df['col2'] == 'Z', 'green', 'red') In [210]: df Out[210]: col1 col2 color 0 A Z green 1 B Z green 2 B X red 3 C Y red If you have multiple conditions, you can use numpy.select() to achieve that. Say corresponding to three condi- tions there are three choice of colors, with a fourth color as a fallback, you can do the following. In [211]: conditions = [ .....: (df['col2'] == 'Z') & (df['col1']