在 if 循环中重命名 pandas 条目

Question

I got a dataframe that looks like that:

 appended_data
...
...
Out[24]: 
         Unnamed: 0  Region     GeneID  DistanceValue
229676       229676     8.0   69013161       0.972237
229677       229677     8.0   75931496       0.978386
229678       229678     8.0   73424023       0.982667
229679       229679     8.0   74736195       0.987057
280716       280716    10.0   69782778       0.971998
            ...     ...        ...            ...
1097355     1097355    42.0     633685       0.972917
1097356     1097356    42.0   68301216       0.974058
1097357     1097357    42.0  100146162       0.977017
1097358     1097358    42.0   74734794       0.985398
1097359     1097359    42.0     633694       0.992054

[136 rows x 4 columns]

I want to rename the entries in Region in a if-loop:

Like.. region 42 should become the name of the 42. entry of layernames. I tried:

import pandas as pd
appended_data = pd.read_csv('/home/anja/0415_pyversion.csv')
with open('/home/anja/Schreibtisch/Master/ABA/layer2_names.txt') as f:
    layernames = [line.strip() for line in f.read().split('\n')]


for i in range(43):
    if (appended_data.Region == i).all:
     (appended_data.Region == i).all = layernames[i] 

I tried that, I got no error message, but my entries aren't renamed.

.. if do

print((appended_data.Region == i).all)

I get:

Name: Region, Length: 136, dtype: bool>
<bound method Series.all of 229676     False
229677     False
229678     False
229679     False
280716     False

1097355     True
1097356     True
1097357     True
1097358     True
1097359     True
Name: Region, Length: 136, dtype: bool>

Answer 1

Series.map

If you ever use for i in range(..) as an iterator then alarm bells should go off as Python as a built in iterator which can be accessed via enumerate

You've not given a minimum example of what layer names is, but it seems like it's a list.

we can leverage the map function with a dictionary and make a one time operation to replace all the region name.s

Setup

import pandas as pd

s = ['a','b','c'] # your layer_name list. 

layer_dict = {k : v for (k,v) in enumerate(s)}
# {0: 'a', 1: 'b', 2: 'c'}
df = pd.DataFrame([0,1,2],columns=list('A'))

In Action:

df['B'] = df['A'].map(layer_dict)
print(df)
   A  B
0  0  a
1  1  b
2  2  c