pandas.DataFrame.div#

DataFrame.div(other,axis='columns',level=None,fill_value=None)[source]#

Get Floating division of dataframe and other, element-wise (binary operatortruediv).

Equivalent todataframe/other, but with support to substitute a fill_valuefor missing data in one of the inputs. With reverse version,rtruediv.

Among flexible wrappers (add,sub,mul,div,floordiv,mod,pow) toarithmetic operators:+,-,*,/,//,%,**.

Parameters:

otherscalar, sequence, Series, dict or DataFrame

Any single or multiple element data structure, or list-like object.

axis{0 or ‘index’, 1 or ‘columns’}

Whether to compare by the index (0 or ‘index’) or columns.(1 or ‘columns’). For Series input, axis to match Series index on.

levelint or label

Broadcast across a level, matching Index values on thepassed MultiIndex level.

fill_valuefloat or None, default None

Fill existing missing (NaN) values, and any new element needed forsuccessful DataFrame alignment, with this value before computation.If data in both corresponding DataFrame locations is missingthe result will be missing.

Returns:

DataFrame

Result of the arithmetic operation.

See also

DataFrame.add

Add DataFrames.

DataFrame.sub

Subtract DataFrames.

DataFrame.mul

Multiply DataFrames.

DataFrame.div

Divide DataFrames (float division).

DataFrame.truediv

Divide DataFrames (float division).

DataFrame.floordiv

Divide DataFrames (integer division).

DataFrame.mod

Calculate modulo (remainder after division).

DataFrame.pow

Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>>df=pd.DataFrame({'angles':[0,3,4],...'degrees':[360,180,360]},...index=['circle','triangle','rectangle'])>>>df           angles  degreescircle          0      360triangle        3      180rectangle       4      360

Add a scalar with operator version which return the sameresults.

>>>df+1           angles  degreescircle          1      361triangle        4      181rectangle       5      361

>>>df.add(1)           angles  degreescircle          1      361triangle        4      181rectangle       5      361

Divide by constant with reverse version.

>>>df.div(10)           angles  degreescircle        0.0     36.0triangle      0.3     18.0rectangle     0.4     36.0

>>>df.rdiv(10)             angles   degreescircle          inf  0.027778triangle   3.333333  0.055556rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>>df-[1,2]           angles  degreescircle         -1      358triangle        2      178rectangle       3      358

>>>df.sub([1,2],axis='columns')           angles  degreescircle         -1      358triangle        2      178rectangle       3      358

>>>df.sub(pd.Series([1,1,1],index=['circle','triangle','rectangle']),...axis='index')           angles  degreescircle         -1      359triangle        2      179rectangle       3      359

Multiply a dictionary by axis.

>>>df.mul({'angles':0,'degrees':2})            angles  degreescircle           0      720triangle         0      360rectangle        0      720

>>>df.mul({'circle':0,'triangle':2,'rectangle':3},axis='index')            angles  degreescircle           0        0triangle         6      360rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>>other=pd.DataFrame({'angles':[0,3,4]},...index=['circle','triangle','rectangle'])>>>other           anglescircle          0triangle        3rectangle       4

>>>df*other           angles  degreescircle          0      NaNtriangle        9      NaNrectangle      16      NaN

>>>df.mul(other,fill_value=0)           angles  degreescircle          0      0.0triangle        9      0.0rectangle      16      0.0

Divide by a MultiIndex by level.

>>>df_multindex=pd.DataFrame({'angles':[0,3,4,4,5,6],...'degrees':[360,180,360,360,540,720]},...index=[['A','A','A','B','B','B'],...['circle','triangle','rectangle',...'square','pentagon','hexagon']])>>>df_multindex             angles  degreesA circle          0      360  triangle        3      180  rectangle       4      360B square          4      360  pentagon        5      540  hexagon         6      720

>>>df.div(df_multindex,level=1,fill_value=0)             angles  degreesA circle        NaN      1.0  triangle      1.0      1.0  rectangle     1.0      1.0B square        0.0      0.0  pentagon      0.0      0.0  hexagon       0.0      0.0