Modify dataframes using apply() with loop in pandas-CodePudding

I have a df called 'records' filled with numbers (0-5) as shown.

One of its column is shown with values 0-3.

I want to loop through the dataframe and change all values above 1 to 1 so there will be only 0s and 1s. I came up with this line of code but unsure how to iterate through all df values with apply:

records= records.apply(lambda x: 1 if x>1 for x in "something")

CodePudding user response：

Convert as bool and as int:

records = records.astype(bool).astype(int)

Example:

>>> records
   A  B  C  D  E
0  2  3  4  1  5
1  0  0  2  5  2
2  1  1  2  0  3
3  0  3  5  4  1
4  2  3  2  0  3
5  2  1  5  2  0
6  0  3  1  3  4
7  1  3  3  2  2
8  1  3  1  5  1
9  4  5  3  4  4

>>> records.astype(bool).astype(int)
   A  B  C  D  E
0  1  1  1  1  1
1  0  0  1  1  1
2  1  1  1  0  1
3  0  1  1  1  1
4  1  1  1  0  1
5  1  1  1  1  0
6  0  1  1  1  1
7  1  1  1  1  1
8  1  1  1  1  1
9  1  1  1  1  1

CodePudding user response：

You can also use df.where:

df = pd.DataFrame({'A': [1, 0, 2, 5],
                   'B': [0, 2, 3, 4],
                   })

print (df.where(df==0, 1))

   A  B
0  1  0
1  0  1
2  1  1
3  1  1

CodePudding user response：

Also, provide the axis=1 argument in the apply method.

CodePudding user response：

You can use .ne(0) like below:

>>> df = pd.DataFrame({'A': [1, 0, 2, 5],'B': [0, 2, 3, 4], 'C': [3,4,0,0], 'D':[0,1,1,1]})
>>> df
    A   B   C   D
0   1   0   3   0
1   0   2   4   1
2   2   3   0   1
3   5   4   0   1

>>> df[df.ne(0)] = 1
>> df
    A   B   C   D
0   1   0   1   0
1   0   1   1   1
2   1   1   0   1
3   1   1   0   1

CodePudding user response：

You can also use the .clip() function, setting upper limit equals 1, as follows:

records = records.clip(upper=1)

Taking Corralien's sample data, we have

print(records)

   A  B  C  D  E
0  2  3  4  1  5
1  0  0  2  5  2
2  1  1  2  0  3
3  0  3  5  4  1
4  2  3  2  0  3
5  2  1  5  2  0
6  0  3  1  3  4
7  1  3  3  2  2
8  1  3  1  5  1
9  4  5  3  4  4

records = records.clip(upper=1)

print(records)

   A  B  C  D  E
0  1  1  1  1  1
1  0  0  1  1  1
2  1  1  1  0  1
3  0  1  1  1  1
4  1  1  1  0  1
5  1  1  1  1  0
6  0  1  1  1  1
7  1  1  1  1  1
8  1  1  1  1  1
9  1  1  1  1  1