Changing Text in a Data Frame

How to replace text in a string column of a Pandas dataframe?

Use the vectorised str method replace:

df['range'] = df['range'].str.replace(',','-')

df
      range
0    (2-30)
1  (50-290)

EDIT: so if we look at what you tried and why it didn't work:

df['range'].replace(',','-',inplace=True)

from the docs we see this description:

str or regex: str: string exactly matching to_replace will be replaced
with value

So because the str values do not match, no replacement occurs, compare with the following:

df = pd.DataFrame({'range':['(2,30)',',']})
df['range'].replace(',','-', inplace=True)

df['range']

0    (2,30)
1         -
Name: range, dtype: object

here we get an exact match on the second row and the replacement occurs.

How to modify a Pandas Column filled text

You can perform string operations on Pandas series by appending .str to the series name. Here are some examples:

>>> df = pd.DataFrame([{'A': 'Label1', 'B': '$12.00'},
...                    {'A': 'Label2', 'B': '$14.00'},
...                    {'A': 'Label1', 'B': '$9.00'},
...                    {'A': 'Label2', 'B': '$8.00'}])
>>> df.B.str.replace('$','')
0    12.00
1    14.00
2     9.00
3     8.00
Name: B, dtype: object
>>> df.A.str[-1:]
0    1
1    2
2    1
3    2
Name: A, dtype: object
>>> df.A.str[1:]
0    abel1
1    abel2
2    abel1
3    abel2
Name: A, dtype: object
>>> df.B.str.len()
0    6
1    6
2    5
3    5
Name: B, dtype: int64

Pandas documentation: Working with Text Data

Change a dataframe column from text to numbers in pandas dataframe

Use pandas.Series.apply with lambda:

ozone_data['pluie'] = ozone_data['pluie'].apply(lambda x: 1 if x == 'Pluie' else 2)

or use numpy.where

df['pluie'] = np.where(df['pluie'] == 'Pluie', 1,2)

There are few more ways to achieve the same result.

How to change the font-size of text in dataframe using pandas

You can set one or more properties for each cell using set_properties().

df = pd.DataFrame({
    'date': ('2019-11-29', '2016-11-28'),
    'price': (0, 1),
})

df = df.style.set_properties(**{
    'background-color': 'grey',
    'font-size': '20pt',
})
df.to_excel('test.xlsx', engine='openpyxl')

Also you can use apply() method to customize specific cells:

def custom_styles(val):
    # price column styles
    if val.name == 'price':
        styles = []
        # red prices with 0
        for i in val:
            styles.append('color: %s' % ('red' if i == 0 else 'black'))
        return styles
    # other columns will be yellow
    return ['background-color: yellow'] * len(val)

df = pd.DataFrame(...)
df = df.style.apply(custom_styles)
df.to_excel('test.xlsx', engine='openpyxl')

Also you can use applymap method which works elementwise. You can find more examples in docs.

Hope this helps.

edit string text in dataframe variable

This is also less elegant than desired, but should work for multiple knots

output %>%
  mutate(is_spline = grepl("^rcs\\(.*?, \\d\\)", term),
         n_term = str_count(term, "'") + 1,
         pre = ifelse(is_spline, paste0('s', n_term, ' '), ""),
         term = paste0(pre, gsub("(^rcs\\(.*?, \\d\\))|(\\'+$)", "", term))) %>%
  select(-is_spline, -n_term, -pre)
#> # A tibble: 7 x 5
#>   term         estimate std.error statistic p.value
#>   <chr>           <dbl>     <dbl>     <dbl>   <dbl>
#> 1 s1 age      -0.00306    0.0219     -0.140 0.889  
#> 2 s2 age       0.0154     0.0261      0.592 0.554  
#> 3 sex         -0.525      0.192      -2.74  0.00620
#> 4 ph.ecog      0.421      0.131       3.22  0.00128
#> 5 s1 meal.cal -0.000416   0.00104    -0.400 0.689  
#> 6 s2 meal.cal  0.00118    0.00232     0.509 0.611  
#> 7 s3 meal.cal -0.00659    0.0114     -0.577 0.564 

How to change text from specific cells in a dataframe?

You can do this as follows:

foo[[6]][[50]] <- "BT"
foo[[6]][[51]] <- "BT"

where foo is the name of your data frame (your screenshot doesn't show that). Regardless of name values, you can always address individual cells in a data frame with the [[column]][[row]] syntax.

How to change text in specific column in panda data frame?

You can use .str.replace() on the WIKIURL series. Example -

df['WIKIURL'] = df['WIKIURL'].str.replace('/wiki/','')

---

Demo -

In [95]: df
Out[95]:
            WIKIURL  NUMBER
0      /wiki/blabla       9
1   /wiki/bladiebla       8
2   /wiki/blablabla       1
3   /wiki/kipapapap       2
4  /wiki/wqeqrwtyee       3
5  /wiki/soduyfhlas       1

In [96]: df['WIKIURL'] = df['WIKIURL'].str.replace('/wiki/','')

In [97]: df
Out[97]:
      WIKIURL  NUMBER
0      blabla       9
1   bladiebla       8
2   blablabla       1
3   kipapapap       2
4  wqeqrwtyee       3
5  soduyfhlas       1

---

Related Topics