## How to subtract rows of one pandas data frame from another?

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The operation that I want to do is similar to merger. For example, with the `inner`

merger we get a data frame that contains rows that are present in the first AND second data frame. With the `outer`

merger we get a data frame that are present EITHER in the first OR in the second data frame.

What I need is a data frame that contains rows that are present in the first data frame AND NOT present in the second one? Is there a fast and elegant way to do it?

How about something like the following?

print df1 Team Year foo 0 Hawks 2001 5 1 Hawks 2004 4 2 Nets 1987 3 3 Nets 1988 6 4 Nets 2001 8 5 Nets 2000 10 6 Heat 2004 6 7 Pacers 2003 12 print df2 Team Year foo 0 Pacers 2003 12 1 Heat 2004 6 2 Nets 1988 6

As long as there is a non-key commonly named column, you can let the added on sufffexes do the work (if there is no non-key common column then you could create one to use temporarily ... `df1['common'] = 1`

and `df2['common'] = 1`

):

new = df1.merge(df2,on=['Team','Year'],how='left') print new[new.foo_y.isnull()] Team Year foo_x foo_y 0 Hawks 2001 5 NaN 1 Hawks 2004 4 NaN 2 Nets 1987 3 NaN 4 Nets 2001 8 NaN 5 Nets 2000 10 NaN

Or you can use `isin`

but you would have to create a single key:

df1['key'] = df1['Team'] + df1['Year'].astype(str) df2['key'] = df1['Team'] + df2['Year'].astype(str) print df1[~df1.key.isin(df2.key)] Team Year foo key 0 Hawks 2001 5 Hawks2001 2 Nets 1987 3 Nets1987 4 Nets 2001 8 Nets2001 5 Nets 2000 10 Nets2000 6 Heat 2004 6 Heat2004 7 Pacers 2003 12 Pacers2003

**How to subtract all rows in a dataframe with a row from another ,** Pandas NDFrames generally try to perform operations on items with matching indices. df - df2 only performs subtraction on the first row, Pandas - Dropping multiple empty columns. python,pandas. You can just subscript the columns: df = df[df.columns[:11]] This will return just the first 11 columns or you can do: df.drop(df.columns[11:], axis=1) To drop all the columns after the 11th one.

Consider Following:

- df_one is first DataFrame
- df_two is second DataFrame

Present in **First DataFrame** and **Not in Second DataFrame**

Solution: by **Index**
```
df = df_one[~df_one.index.isin(df_two.index)]
```

**index** can be replaced by required **column** upon which you wish to do exclusion.
In above example, I've used index as a reference between both Data Frames

Additionally, you can also use a more complex query using boolean pandas.Series to solve for above.

**pandas.DataFrame.subtract,** Equivalent to dataframe - other , but with support to substitute a fill_value for Broadcast across a level, matching Index values on the passed MultiIndex level. Pandas dataframe.subtract () function is used for finding the subtraction of dataframe and other, element-wise. This function is essentially same as doing dataframe - other but with a support to substitute for missing data in one of the inputs. Syntax: DataFrame.subtract (other, axis=’columns’, level=None, fill_value=None)

You could run into errors if your non-index column has cells with NaN.

print df1 Team Year foo 0 Hawks 2001 5 1 Hawks 2004 4 2 Nets 1987 3 3 Nets 1988 6 4 Nets 2001 8 5 Nets 2000 10 6 Heat 2004 6 7 Pacers 2003 12 8 Problem 2112 NaN print df2 Team Year foo 0 Pacers 2003 12 1 Heat 2004 6 2 Nets 1988 6 3 Problem 2112 NaN new = df1.merge(df2,on=['Team','Year'],how='left') print new[new.foo_y.isnull()] Team Year foo_x foo_y 0 Hawks 2001 5 NaN 1 Hawks 2004 4 NaN 2 Nets 1987 3 NaN 4 Nets 2001 8 NaN 5 Nets 2000 10 NaN 6 Problem 2112 NaN NaN

The problem team in 2112 has no value for foo in either table. So, the left join here will falsely return that row, which matches in both DataFrames, as not being present in the right DataFrame.

**Solution:**

What I do is to add a unique column to the inner DataFrame and set a value for all rows. Then when you join, you can check to see if that column is NaN for the inner table to find unique records in the outer table.

df2['in_df2']='yes' print df2 Team Year foo in_df2 0 Pacers 2003 12 yes 1 Heat 2004 6 yes 2 Nets 1988 6 yes 3 Problem 2112 NaN yes new = df1.merge(df2,on=['Team','Year'],how='left') print new[new.in_df2.isnull()] Team Year foo_x foo_y in_df1 in_df2 0 Hawks 2001 5 NaN yes NaN 1 Hawks 2004 4 NaN yes NaN 2 Nets 1987 3 NaN yes NaN 4 Nets 2001 8 NaN yes NaN 5 Nets 2000 10 NaN yes NaN

NB. The problem row is now correctly filtered out, because it has a value for in_df2.

Problem 2112 NaN NaN yes yes

**Python,** Parameters : other : Series, DataFrame, or constant axis : For Series input, axis to match Series index on level : Broadcast across a level, matching Index values pandas.DataFrame.subtract ¶ DataFrame.subtract(self, other, axis='columns', level=None, fill_value=None) [source] ¶ Get Subtraction of dataframe and other, element-wise (binary operator sub). Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs.

I suggest using parameter 'indicator' in merge. Also if 'on' is None this defaults to the intersection of the columns in both DataFrames.

new = df1.merge(df2,how='left', indicator=True) # adds a new column '_merge' new = new[(new['_merge']=='left_only')].copy() #rows only in df1 and not df2 new = new.drop(columns='_merge').copy() Team Year foo 0 Hawks 2001 5 1 Hawks 2004 4 2 Nets 1987 3 4 Nets 2001 8 5 Nets 2000 10

Reference: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html

indicator : boolean or string, default False If True, adds a column to output DataFrame called "_merge" with information on the source of each row. Information column is Categorical-type and takes on a value of "left_only" for observations whose merge key only appears in ‘left’ DataFrame, "right_only" for observations whose merge key only appears in ‘right’ DataFrame, and "both" if the observation’s merge key is found in both.

**Subtracting a pandas DataFrame from another DataFrame ,** The sub() method supports passing a parameter for missing values(np.nan, None). Example: import pandas as pd. Subtracting one column from another in Pandas created memory probems and a solution I had two datasets with about 17 million observations for different variables in each. One was an event file (admissions to hospitals, when, what and so on).

**How to add or subtract two columns and put the results in a new ,** How do I add or subtract two columns and put the results in a new column in a Pandas How can I Filter Rows of a Pandas DataFrame by Column Value? Data Frame before Adding Row-Data Frame after Adding Row-For more examples refer to Add a row at top in pandas DataFrame Row Deletion: In Order to delete a row in Pandas DataFrame, we can use the drop() method. Rows is deleted by dropping Rows by index label.

**Adding and subtract inbetween row inputs and value equal to the ,** I tried another method and it worked, but didn't came as I want. import pandas as pd #a sample dictionary data = {'x1':[1,0,4,5,8,1], 'x2':[3 x1 x2 x3 y 0 1 3 4 True 1 0 4 5 False 2 4 5 1 False 3 5 6 -2 False 4 8 8 4 False 5 1 9 5 0 are looping in the rows of data frame while j is less than data frame's length The row with index 3 is not included in the extract because that’s how the slicing syntax works. Note also that row with index 1 is the second row. Row with index 2 is the third row and so on. If you’re wondering, the first row of the dataframe has an index of 0. That’s just how indexing works in Python and pandas.

**Subtracting values across grouped data frames in Pandas,** Pandas subtract two dataframes on index. pandas.DataFrame.subtract, Any single or multiple element data structure, or list-like object. axis{0 or 'index', 1 or Another DataFrame; Steps to Select Rows from Pandas DataFrame Step 1: Data Setup. Pandas read_csv() is an inbuilt function that is used to import the data from a CSV file and analyze that data in Python. So, we will import the Dataset from the CSV file, and it will be automatically converted to Pandas DataFrame and then select the Data from

##### Comments

- how = 'left'? surely that's not what you want (given your SO score it must be more complex than that)
- Left or right merge gives me a data frame that contains rows that are present in one of the data frames. But I need a data frame that contains rows that are present in one data frame AND NOT present in another one.
- If it is just one merge key then you could do it with
`isin`

and`~`

- I'm laughing to myself actually trying to understand how to move something to object1 from object2 on the condition that the thing is in object one, and NOT in object2. To me, that just sounds like object1 - no operation necessary! I don't think I get the point. Ignore me, sorry, it just made me smile...
- @KarlD., I have more than one merge keys.