如何根据条件替换 Panda 数据框列中的单元格

Question

This post consists of two questions, touching on issues I have encountered when trying to replace elements in a Panda dataframe based on a given condition. I am new with Pandas, so any suggestions will be most helpful.

1: Modifying strings of a certain length

Consider column A in a Panda dataframe object, df:

SSIC
103
1040
1054
1065
107

I want to append the integer 0 to each cell that is of length less than four. That is, I want to obtain:

SSIC
0103
1040
1054
1065
0107

The values are of type float64.

Currently, I have used this method:

SSIC1 = df['SSIC'].astype('int64').astype(str)

for i,n in enumerate(SSIC1):
    if len(SSIC1[i]) == 4:
       SSIC1[i] = '0' + SSIC1[i]

df['SSIC'] = SSIC1

It works, but I wonder if it is rather long-winded. Is there a more direct way to resolve this?

2: Setting with Copy Warning when using where clause

I have the following two columns in a dataframe

A  B
2  1
3  4
4  6
5  4
5  2

I want to replace cells in column A that take on the value 5, with the values on the same row in B.

I have used the where condition:

df['A']=df['A'].where(df['A'] == 5, df['B'], inplace=True)

​But it gives me the following error:

SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if name == ' main ':

This warning did not occur the first time I ran it. Any ideas why it has popped up?

Answer 1

For your first part call the vectorised str.zfill :

In [167]:
df['SSIC'].astype(str).str.zfill(4)

Out[167]:
0    0103
1    1040
2    1054
3    1065
4    0107
Name: SSIC, dtype: object

You may not need the call astype if the dtype is already str

Answer 2

Use the dataframe.at[] function. That is:

df.at[row_index,column_name] = 'the desired assignment'

for example:

df = pd.DataFrame(data=[['iqbal',2,88]],columns=['name','roll','marks'],index=[0])
df.at[0,'name']= 'Joy'

`