How to Change the Name of a Data Frame

Python change name of panda dataframe

You've done what you want already.

This line

df2 = df

makes a copy of the reference to df so both variables point at the same dataframe. It does not copy the dataframe. To verify this:

>>> df2 is df
True

After that, del df does not delete the dataframe, it deletes the variable that is pointing to it.

How can I change the name of a data frame

The truth is that objects in R don't have names per-se. There exists different kinds of environments, including a global one for every process. These environments have lists of names, that point to various objects. Two different names can point to the same object. This is best explained to my knowledge in the environments chapter of Hadley Wickhams Advanced R book
http://adv-r.had.co.nz/Environments.html

So there is no way to change a name of a data frame, because there is nothing to change.

But you can make a new name (like newname) point to the same object (in your case a data frame object) as an given name (like oldname) simply by doing:

   newname <- oldname

Note that if you change one of these variables a new copy will be made and the internal references will no longer be the same. This is due to R's "Copy on modify" semantics. See this post for an explanation: What exactly is copy-on-modify semantics in R, and where is the canonical source?

Hope that helps. I know the pain. Dynamic and functional languages are different than static and procedural languages...

Of course it is possible to calculate a new name for a dataframe and register it in the environment with the assign command - and perhaps you are looking for this. However referring to it afterwards would be rather convoluted.

Example (assuming df is the dataframe in question):

   assign(  paste("city_stats", city_code, sep = ""), df )

As always see the help for assign for more information http://stat.ethz.ch/R-manual/R-devel/library/base/html/assign.html

Edit:
In reply to your edit, and various comments around the problems with using eval(parse(...) you could parse the name like this:

head(get(gear_subset))

Changing category names in a pandas data frame

TL;DR

1. Use Series.cat.rename_categories for categorical variables.

2. Use Series.map for non-categorical variables.

3. Use Series.replace if regex is needed.

   

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1. Series.cat.rename_categories

This option is fastest but requires the Categorical dtype. If you're analyzing categorical variables, this is highly recommended for its speed/memory/semantic benefits.

First convert to Categorical (if not already):

df['Label'] = df['Label'].astype('category')

Then rename via Series.cat.rename_categories:

df['Label'] = df['Label'].cat.rename_categories({'zero': 0, 'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5, 'six': 6, 'seven': 7, 'eight': 8, 'nine': 9})

#                               File Label
# 20936  eight/b63fea9e_nohash_1.wav     8
# 21016  eight/f44f440f_nohash_2.wav     8
# 7423   three/d8ed3745_nohash_0.wav     3
# ...                            ...   ...
# 646     zero/24632875_nohash_0.wav     0

---

2. Series.map

If you can't (or don't want to) use the Categorical dtype, Series.map is the next fastest:

df['Label'] = df['Label'].map({'zero': 0, 'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5, 'six': 6, 'seven': 7, 'eight': 8, 'nine': 9})

#                               File Label
# 20936  eight/b63fea9e_nohash_1.wav     8
# 21016  eight/f44f440f_nohash_2.wav     8
# 7423   three/d8ed3745_nohash_0.wav     3
# ...                            ...   ...
# 646     zero/24632875_nohash_0.wav     0

---

3. Series.replace

This option is slow but offers regex/filling capabilities via the regex and method params.

As a contrived example, say we want less granular labels:

mapping = {
    r'zero|one': '0,1',
    r'two|three': '2,3',
    r'four|five': '4,5',
    r'six|seven': '6,7',
    r'eight|nine': '8,9',
}

Then we can use Series.replace with regex=True:

df['Label'] = df['Label'].replace(mapping, regex=True)

#                               File Label
# 20936  eight/b63fea9e_nohash_1.wav   8,9
# 7423   three/d8ed3745_nohash_0.wav   2,3
# 1103    zero/ad63d93c_nohash_4.wav   0,1
# ...                            ...   ...
# 646     zero/24632875_nohash_0.wav   0,1

Renaming column names in Pandas

Just assign it to the .columns attribute:

>>> df = pd.DataFrame({'$a':[1,2], '$b': [10,20]})
>>> df
   $a  $b
0   1  10
1   2  20

>>> df.columns = ['a', 'b']
>>> df
   a   b
0  1  10
1  2  20

How to rename dataframe in R?

You must use assign to create an object with a name given by a character string and use get to get the object given by that string.

Note that the name with the prefix "supp" will only exist in the function and is discarded on exit.

test <- function(inds){
  a1 <- deparse(substitute(inds))
  a2 <- paste0("supp", a1)
  assign(a2, inds)
  out_df <- get(a2)
  
  print(class(a1))
  print(class(a2))
  print(class(out_df))
  
  out_df
}

head(test(iris))
#> [1] "character"
#> [1] "character"
#> [1] "data.frame"
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0          1.4         0.2  setosa
#> 3          4.7         3.2          1.3         0.2  setosa
#> 4          4.6         3.1          1.5         0.2  setosa
#> 5          5.0         3.6          1.4         0.2  setosa
#> 6          5.4         3.9          1.7         0.4  setosa

^{Created on 2022-03-23 by the reprex package (v2.0.1)}

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