Append suffix to a pandas series index
Question:
Given the following Panda series:
>>>series = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>>series
a 1
b 2
c 3
dtype: int64
Is there a way to produce this?
>>>series.do_something()
a_x 1
b_x 2
c_x 3
dtype: int64
Background
I have a Series that was produced from a DataFrame aggregate function: df.sum()
The indexes are currently the column names, but I want them to be the column names plus _sum, like so:
>>>data
col1_sum 500.00
col2_sum 9324.0
col3_sum 0.2340
dtype: float64
Answers:
In [49]: series.index += '_sum'
In [50]: series
Out[50]:
a_sum 1
b_sum 2
c_sum 3
dtype: int64
For completeness, you can use str.replace.
s.index = s.index.str.replace('(.*)', r'1_sum')
print(s)
a_sum 1
b_sum 2
c_sum 3
dtype: int64
Maybe series.add_suffix('_x') matches what you need!
And series.add_prefix('x_') returns something like this:
x_a 1
x_b 2
x_c 3
dtype: int64
In case anyone is curious about using this for a DataFrame, the snippet below accomplishes the task.
Where df is a pandas DataFrame and ‘C_’ is the desired prefix.
The default behavior of add_prefix for a DataFrame is to add the prefix to the columns. Thus the transpose and then add_prefix and then transpose back.
df = df.T.add_prefix(‘C_’).T
Given the following Panda series:
>>>series = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>>series
a 1
b 2
c 3
dtype: int64
Is there a way to produce this?
>>>series.do_something()
a_x 1
b_x 2
c_x 3
dtype: int64
Background
I have a Series that was produced from a DataFrame aggregate function: df.sum()
The indexes are currently the column names, but I want them to be the column names plus _sum, like so:
>>>data
col1_sum 500.00
col2_sum 9324.0
col3_sum 0.2340
dtype: float64
In [49]: series.index += '_sum'
In [50]: series
Out[50]:
a_sum 1
b_sum 2
c_sum 3
dtype: int64
For completeness, you can use str.replace.
s.index = s.index.str.replace('(.*)', r'1_sum')
print(s)
a_sum 1
b_sum 2
c_sum 3
dtype: int64
Maybe series.add_suffix('_x') matches what you need!
And series.add_prefix('x_') returns something like this:
x_a 1
x_b 2
x_c 3
dtype: int64
In case anyone is curious about using this for a DataFrame, the snippet below accomplishes the task.
Where df is a pandas DataFrame and ‘C_’ is the desired prefix.
The default behavior of add_prefix for a DataFrame is to add the prefix to the columns. Thus the transpose and then add_prefix and then transpose back.
df = df.T.add_prefix(‘C_’).T