Replacing values greater 1 in a large pandas dataframe

You can try this.

Using boolean mask

df.set_index('Report No',inplace=True)
df[df > 1] = 1
df.reset_index()

Report No   Apple   Orange   Lemon   Grape   Pear
One           1       0        1       1      1
Two           1       1        0       1      1
Three         0       0        1       1      1
Four          1       1        1       0      0
Five          1       0        0       1      1
Six           1       1        1       1      0

Or use this if you have some non numeric columns. No need to use set_index and reset_index. This is equivalent to df.select_dtypes('number')

val = df._get_numeric_data()
val[val > 1] = 1
df
Report No   Apple   Orange   Lemon   Grape   Pear
One           1       0        1       1      1
Two           1       1        0       1      1
Three         0       0        1       1      1
Four          1       1        1       0      0
Five          1       0        0       1      1
Six           1       1        1       1      0

df.mask

df.set_index('Report No',inplace=True)
df.mask(df>1,1).reset_index()
Report No   Apple   Orange   Lemon   Grape   Pear
One           1       0        1       1      1
Two           1       1        0       1      1
Three         0       0        1       1      1
Four          1       1        1       0      0
Five          1       0        0       1      1
Six           1       1        1       1      0

np.where

df[df.columns[1:]] = df.iloc[:,1:].where(df.iloc[:,1:] >1 ,1)

np.select

This can be helpful when dealing with multiple conditions. If you want to convert values less than 0 to 0 and values greater than 1 to 1.

df.set_index('Report No', inplace=True)
condlist = [df >= 1, df <= 0] #you can have more conditions and add choices accordingly.
choice = [1, 0] #len(condlist) should be equal to len(choice).
df.loc[:] = np.select(condlist, choice)

Like Jan mentioned use df.clip

Not recommended but you can try this for fun. Using df.astype.

df.set_index('Report No',inplace=True)
df.astype('bool').astype('int')

NOTE: This will only convert falsy values to False and truthy values to True i.e. this will convert 0 to False and anything other than 0 is True even negative numbers.

s = pd.Series([1,-1,0])
s.astype('bool')
0     True
1     True
2    False
dtype: bool

s.astype('bool').astype('int')
0    1
1    1
2    0
dtype: int32

np.sign

When values present are between [0, n] i.e no negative values.

df.loc[:] = np.sign(df)

Use pandas.DataFrame.clip:

new_df = df.clip(0, 1)

EDIT: To exclude the first column by name (this will edit the DataFrame in-place)

mask = df.columns != "Report No"
df.loc[:, mask] = df.loc[:, mask].clip(0, 1)

The fastest and easiest way is to go through all the keys of the datframe and change them using the where function of numpy (library which has to be imported). Then we simply pass as an attribute to that function the condition and the values for when the condition is satisfied or not. In your example it would look like this:

for x in df.keys()[1:]:
   df[x] = np.where(df[x] > 1, 1, df[x])

Note that in the loop I have quited the first key because its values are not integer