Don'ts Leverage the color palettes and keep things simple, ensuring there is a strong contrast between the symbol and the background. Don’t use colors that aren’t in the approved color palette or or primary brand color, please use it sparingly. We prefer to apply PyTorch Orange as a deliberate accent. To achieve the best AA compliance color contrast, PyTorch has a special color palette to best best serve these needs. When applying color in the digital environment; web, app, and social media posts, please reference the digital RGB or hex code equivalent. When printing, please use CMYK

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. Say corresponding to three conditions there are three choice of colors, with a fourth color as a fallback, you can do the following. In [213]: 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 [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 achieve that. Say corresponding to three conditions there are three choice of colors, with a fourth color as a fallback, you can do the following. In [213]: 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 [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. Say corresponding to three conditions there are three choice of colors, with a fourth color as a fallback, you can do the following. In [213]: 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']

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 conditions 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 conditions 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']

(i_color = 'powder' OR i_color = 'khaki') AND (i_units = 'Ounce' OR i_units = 'Oz') AND (i_size = 'medium' OR i_size = 'extra large') ) OR (i_category = 'Women' AND (i_color = 'brown' OR i_color = 'honeydew') AND (i_color = 'floral' OR i_color = 'deep') AND (i_units = 'N/A' OR i_units = 'Dozen') AND (i_size = 'petite' OR i_size = 'large') ) OR (i_category = 'Men' AND (i_color = 'light' OR i_color = 'cornflower') 'Women' AND (i_color = 'midnight' OR i_color = 'snow') AND (i_units = 'Pallet' OR i_units = 'Gross') AND (i_size = 'medium' OR i_size = 'extra large') ) OR (i_category = 'Women' AND (i_color = 'cyan' OR

(i_color = 'powder' OR i_color = 'khaki') AND (i_units = 'Ounce' OR i_units = 'Oz') AND (i_size = 'medium' OR i_size = 'extra large') ) OR (i_category = 'Women' AND (i_color = 'brown' OR i_color = 'honeydew') AND (i_color = 'floral' OR i_color = 'deep') AND (i_units = 'N/A' OR i_units = 'Dozen') AND (i_size = 'petite' OR i_size = 'large') ) OR (i_category = 'Men' AND (i_color = 'light' OR i_color = 'cornflower') 'Women' AND (i_color = 'midnight' OR i_color = 'snow') AND (i_units = 'Pallet' OR i_units = 'Gross') AND (i_size = 'medium' OR i_size = 'extra large') ) OR (i_category = 'Women' AND (i_color = 'cyan' OR

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