Lowercase columns by name using dataframe method

Question

I have a dataframe containing strings and NaNs. I want to str.lower() certain columns by name to_lower = ['b', 'd', 'e']. Ideally I could do it with a method on the whole dataframe, rather than with a method on df[to_lower]. I have

df[to_lower] = df[to_lower].apply(lambda x: x.astype(str).str.lower())

but I would like a way to do it without assigning to the selected columns.

df = pd.DataFrame({'a': ['A', 'a'], 'b': ['B', 'b']})
to_lower = ['a']
df2 = df.copy()
df2[to_lower] = df2[to_lower].apply(lambda x: x.astype(str).str.lower())

Your method doesn't assign the result to the selected columns. It returns a new data frame. If you want to assign them to new columns, df[new_columns] = df[to_lower].apply(lambda x: x.astype(str).str.lower())? — akuiper
– akuiper, Commented Jun 23, 2017 at 3:30
If you don't want to change the to_lower columns, just provide new column names as new_columns. — akuiper
– akuiper, Commented Jun 23, 2017 at 3:34
@Psidom My code above does what I want, but I want a way to do it as a method on df, so I don't have to assign to df[lower], not to avoid overwriting those columns, but because I want to do it as method chaining on df rather than on a subset of it. — Hatshepsut
– Hatshepsut, Commented Jun 23, 2017 at 3:37

akuiper · Accepted Answer · 2017-06-23 03:44:06Z

1

You can use assign method and unpack the result as keyword argument:

df = pd.DataFrame({'a': ['A', 'a'], 'b': ['B', 'b'], 'c': ['C', 'c']})
to_lower = ['a', 'b']

df.assign(**df[to_lower].apply(lambda x: x.astype(str).str.lower()))

#   a   b   c
#0  a   b   C
#1  a   b   c

answered Jun 23, 2017 at 3:44

akuiper

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Hatshepsut Over a year ago

Is it possible to do this without using the df name inside assign, so I can do it in the middle of a chain of methods?

John Zwinck · Accepted Answer · 2017-06-23 03:42:43Z

0

You want this:

for column in to_lower:
    df[column] = df[column].str.lower()

This is far more efficient assuming you have more rows than columns.

answered Jun 23, 2017 at 3:42

John Zwinck

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1 Comment

Hatshepsut Over a year ago

Is it possible to do this returning a new dataframe rather than mutating the original?

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