从整个 DataFrame 中删除空格

Question

i have a dataframe, 22 columns and 65 rows. The data comes in from csv file. Each of the values with dataframe has an extra unwanted whitespace. So if i do a loop on 'Year' column with a Len() i get

2019  5
2019  5
2018  5
...

this 1 extra whitespace appears throughout DF in every value. I tried running a.strip() on DF but no attribute exists

i tried a 'for each df[column].str.strip() but there are various data types in each column... dtypes: float64(6), int64(4), object(14), so this errors.

any ideas on how to apply a function for entire dataframe, and if so, what function/method? if not what is best way to handle?

Answer 1

you should use apply() function in order to do this:

df['Year'] = df['Year'].apply(lambda x:x.strip() )

you can apply this function on each column separately:

for column in df.columns:
    df[column] = df[column].apply(lambda x:x.strip() )

Answer 2

Handle the error:

for col in df.columns:
    try:
        df[col] = df[col].str.strip()
    except AttributeError:
        pass

---

Normally, I'd say select the object dtypes, but that can still be problematic if the data are messy enough to store numeric data in an object container.

import pandas as pd

df = pd.DataFrame({'foo': [1, 2, 3], 'bar': ['seven ']*3})
df['foo2'] = df.foo.astype(object)

for col in df.select_dtypes('object'):
    df[col] = df[col].str.strip()
#AttributeError: Can only use .str accessor with string values!

Answer 3

Try this:

for column in df.columns:
    df[column] = df[column].apply(lambda x: str(x).replace('  ', ' '))

Answer 4

Why not try this?

for column in df.columns:
    df[column] = df[column].apply(lambda x: str(x).strip())