Is there a version of this but using a dictionary instead of a list? Following the example in the link what I want to do is idx.rename({'kind':'species'}).
If it matters, this is how my data frame looks like:
k_CFD (%) k_CFD (%) error
run_name CFD
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 68.529412 9.576598
MCP-PMT 72.058824 21.711526
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 77.647059 14.783042
MCP-PMT 64.411765 20.477804
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 73.235294 6.840427
MCP-PMT 74.117647 14.379625
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 50.294118 9.369614
MCP-PMT 64.117647 15.786002
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 56.176471 11.285471
MCP-PMT 46.764706 15.709597
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 62.058824 16.288946
MCP-PMT 68.823529 18.383904
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 78.235294 8.693637
MCP-PMT 65.294118 18.947342
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 85.588235 7.463518
MCP-PMT 81.470588 14.591893
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 30.882353 5.703612
MCP-PMT 48.529412 15.595717
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 62.647059 12.137801
MCP-PMT 56.470588 19.829936
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 71.764706 11.926692
MCP-PMT 63.823529 22.158480
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 87.058824 4.624973
MCP-PMT 71.764706 22.626157
20230104124758_TI107_HDO6034-MS_WithInterpolati... DUT 75.588235 7.859052
MCP-PMT 72.058824 14.094790
Note that I want a solution independent of the ordering of the columns in the index, that's why I want to use a dictionary and not a list. I want to rename the index column 'CFD' to something else, independently of its position.
You approach should work, make sure to assign the output or use inplace=True:
df.index = df.index.rename({'kind': 'species'})
Example input:
0
kind type
1 A 0
2 B 1
Output:
0
species type
1 A 0
2 B 1