Renaming columns in pandas

Question

I have a DataFrame using pandas and column labels that I need to edit to replace the original column labels.

I\'d like to change the column names in a DataFrame

Answer 1

One line or Pipeline solutions

I'll focus on two things:

1. OP clearly states

   I have the edited column names stored it in a list, but I don't know how to replace the column names.

   I do not want to solve the problem of how to replace '$' or strip the first character off of each column header. OP has already done this step. Instead I want to focus on replacing the existing columns object with a new one given a list of replacement column names.

2. df.columns = new where new is the list of new columns names is as simple as it gets. The drawback of this approach is that it requires editing the existing dataframe's columns attribute and it isn't done inline. I'll show a few ways to perform this via pipelining without editing the existing dataframe.

---

Setup 1
To focus on the need to rename of replace column names with a pre-existing list, I'll create a new sample dataframe df with initial column names and unrelated new column names.

df = pd.DataFrame({'Jack': [1, 2], 'Mahesh': [3, 4], 'Xin': [5, 6]})
new = ['x098', 'y765', 'z432']

df

   Jack  Mahesh  Xin
0     1       3    5
1     2       4    6

---

Solution 1
pd.DataFrame.rename

It has been said already that if you had a dictionary mapping the old column names to new column names, you could use pd.DataFrame.rename.

d = {'Jack': 'x098', 'Mahesh': 'y765', 'Xin': 'z432'}
df.rename(columns=d)

   x098  y765  z432
0     1     3     5
1     2     4     6

However, you can easily create that dictionary and include it in the call to rename. The following takes advantage of the fact that when iterating over df, we iterate over each column name.

# given just a list of new column names
df.rename(columns=dict(zip(df, new)))

   x098  y765  z432
0     1     3     5
1     2     4     6

This works great if your original column names are unique. But if they are not, then this breaks down.

---

Setup 2
non-unique columns

df = pd.DataFrame(
    [[1, 3, 5], [2, 4, 6]],
    columns=['Mahesh', 'Mahesh', 'Xin']
)
new = ['x098', 'y765', 'z432']

df

   Mahesh  Mahesh  Xin
0       1       3    5
1       2       4    6

---

Solution 2
pd.concat using the keys argument

First, notice what happens when we attempt to use solution 1:

df.rename(columns=dict(zip(df, new)))

   y765  y765  z432
0     1     3     5
1     2     4     6

We didn't map the new list as the column names. We ended up repeating y765. Instead, we can use the keys argument of the pd.concat function while iterating through the columns of df.

pd.concat([c for _, c in df.items()], axis=1, keys=new) 

   x098  y765  z432
0     1     3     5
1     2     4     6

---

Solution 3
Reconstruct. This should only be used if you have a single dtype for all columns. Otherwise, you'll end up with dtype object for all columns and converting them back requires more dictionary work.

Single dtype

pd.DataFrame(df.values, df.index, new)

   x098  y765  z432
0     1     3     5
1     2     4     6

Mixed dtype

pd.DataFrame(df.values, df.index, new).astype(dict(zip(new, df.dtypes)))

   x098  y765  z432
0     1     3     5
1     2     4     6

---

Solution 4
This is a gimmicky trick with transpose and set_index. pd.DataFrame.set_index allows us to set an index inline but there is no corresponding set_columns. So we can transpose, then set_index, and transpose back. However, the same single dtype versus mixed dtype caveat from solution 3 applies here.

Single dtype

df.T.set_index(np.asarray(new)).T

   x098  y765  z432
0     1     3     5
1     2     4     6

Mixed dtype

df.T.set_index(np.asarray(new)).T.astype(dict(zip(new, df.dtypes)))

   x098  y765  z432
0     1     3     5
1     2     4     6

---

Solution 5
Use a lambda in pd.DataFrame.rename that cycles through each element of new
In this solution, we pass a lambda that takes x but then ignores it. It also takes a y but doesn't expect it. Instead, an iterator is given as a default value and I can then use that to cycle through one at a time without regard to what the value of x is.

df.rename(columns=lambda x, y=iter(new): next(y))

   x098  y765  z432
0     1     3     5
1     2     4     6

And as pointed out to me by the folks in sopython chat, if I add a * in between x and y, I can protect my y variable. Though, in this context I don't believe it needs protecting. It is still worth mentioning.

df.rename(columns=lambda x, *, y=iter(new): next(y))

   x098  y765  z432
0     1     3     5
1     2     4     6