如何使用熊猫读取（Python）重命名数据框中的行？

Question

I want to rename rows in python program (version - spyder 3 - python 3.6 ) . At this point I have something like that:

import pandas as pd
data = pd.read_csv(filepath, delim_whitespace = True, header = None)

Before that i wanted to rename my columns:

data.columns = ['A', 'B', 'C']

It gave me something like that.

    A   B   C  
0   1   n   1  
1   1   H   0  
2   2   He  1  
3   3   Be  2  

But now, I want to rename rows. I want:

     A   B   C  
n    1   n   1  
H    1   H   0  
He   2   He  1  
Be   3   Be  2

How can I do it? The main idea is to rename every row created by pd.read by the data in the B column. I tried something like this:

for rows in data:
    data.rename(index={0:'df.loc(index, 'B')', 1:'one'})

but it's not working.

Any ideas? Maybe just replace the data frame rows by column B? How?

Answer 1

I think need set_index with rename_axis :

df1 = df.set_index('B', drop=False).rename_axis(None)

Solution with rename and dictionary:

df1 = df.rename(dict(zip(df.index, df['B'])))

print (dict(zip(df.index, df['B'])))
{0: 'n', 1: 'H', 2: 'He', 3: 'Be'}

If default RangeIndex solution should be:

df1 = df.rename(dict(enumerate(df['B'])))

print (dict(enumerate(df['B'])))
{0: 'n', 1: 'H', 2: 'He', 3: 'Be'}

Output :

print (df1)
    A   B  C
n   1   n  1
H   1   H  0
He  2  He  1
Be  3  Be  2

EDIT:

If dont want column B solution is with read_csv by parameter index_col :

import pandas as pd

temp=u"""1 n 1
1 H 0
2 He 1
3 Be 2"""
#after testing replace 'pd.compat.StringIO(temp)' to 'filename.csv'
df = pd.read_csv(pd.compat.StringIO(temp), delim_whitespace=True, header=None, index_col=[1])
print (df)
    0  2
1       
n   1  1
H   1  0
He  2  1
Be  3  2

Answer 2

I normally rename my rows in my dataset by following these steps.

import pandas as pd

df=pd.read_csv("zzzz.csv")


#in a dataframe it is hard to change the names of our rows so,
df.transpose()

#this changes all the rows to columns

df.columns=["","",.....]
# make sure the length of this and the length of columns are same ie dont skip any names.

#Once you are done renaming them: 

df.transpose()

#We get our original dataset with changed row names.

Answer 3

just put colnames into "names" when reading

import pandas as pd

df = pd.read_csv('filename.csv', names=["colname A", "colname B"])