This is my file.txt:
Egg and Bacon;
Egg, sausage and Bacon
Egg and Spam;
Spam Egg Sausage and Spam;
Egg, Bacon and Spam;
I wanna convert the newLine '\n' to ' $ '. I just used:
f = open[fileName]
text = f.read[]
text = text.replace['\n',' $ ']
print[text]
This is my output:
$ Spam Egg Sausage and Spam;
and my output must be like:
Egg and Bacon; $ Egg, sausage and Bacon $ Egg ...
What am I doing wrong? I'm using #-*- encoding: utf-8 -*-
Thank you.
asked Mar 25, 2015 at 9:04
4
It is possible that your newlines are represented as \r\n. In order to replace them you should do:
text.replace['\r\n', ' $ ']
For a portable solution that works on both UNIX-like systems [which uses \n] and Windows [which uses \r\n], you can substitute the text using a regex:
>>> import re
>>> re.sub['\r?\n', ' $ ', 'a\r\nb\r\nc']
'a $ b $ c'
>>> re.sub['\r?\n', ' $ ', 'a\nb\nc']
'a $ b $ c'
answered Mar 25, 2015 at 9:14
enrico.bacisenrico.bacis
29.3k10 gold badges85 silver badges114 bronze badges
2
You can use splitlines.
lines = """Egg and Bacon;
Egg, sausage and Bacon
Egg and Spam;
Spam Egg Sausage and Spam;
Egg, Bacon and Spam;"""
print[" $ ".join[lines.splitlines[]]]
Egg and Bacon; $ Egg, sausage and Bacon $ Egg and Spam; $ Spam Egg Sausage and Spam; $ Egg, Bacon and Spam;
Or simply use rstrip and join on the file object without reading all into memory:
with open["in.txt"] as f:
print[" $ ".join[line.rstrip[] for line in f]]
Egg and Bacon; $ Egg, sausage and Bacon $ Egg and Spam; $ Spam Egg Sausage and Spam; $ Egg, Bacon and Spam;
Which is a much more efficient solution than reading all the file into memory and using a regex. You should also always use with to open your files as it closes them automatically.
rstrip will remove \n
\r\n etc..
In [41]: s = "foo\r\n"
In [42]: s.rstrip[]
Out[42]: 'foo'
In [43]: s = "foo\n"
In [44]: s.rstrip[]
Out[44]: 'foo'
answered Mar 25, 2015 at 9:48
text = text.replace['\\n', '']
answered Oct 9, 2020 at 15:19
1
By using replace[] or fillna[] methods you can replace NaN values with Blank/Empty string in Pandas DataFrame. NaN stands for Not A Number and is one of the common ways to represent the missing data value in Python/Pandas DataFrame. Sometimes we would be required to convert/replace any missing values with the values that make sense like replacing with zero’s for numeric columns and blank or empty for string-type columns.
In this panda DataFrame article, I will
explain how to convert single or multiple [all columns from the list] NaN columns values to blank/empty strings using several ways with examples.
If you are in a hurry, below are some of the quick examples of how to replace NaN with a blank/empty string in Pandas DataFrame.
# Below are quick examples
# Replace all Nan values to empty string
df2 = df.replace[np.nan, '', regex=True]
print[df2]
# Using multiple columns
df2 = df[['Courses','Fee' ]] = df[['Courses','Fee' ]].fillna['']
print[df2]
# Using pandas.DataFrame.fillna[] to replace nan values
df2 = df.fillna[""]
print[df2]
# Using pandas replace nan with null
df2 = df.fillna['', inplace=True]
print[df2]
# Pandas single column using replace nan empty string
df2 = df.Courses.fillna['']
print[df2]
# Using Courses column replace nan with Zeros
df2 = df['Courses']=df['Courses'].fillna[0]
print[df2]
# Using Discount column to replace nan with Zeros
df2 = df['Discount']=df['Discount'].fillna[0]
print[df2]
# Remove the nan and fill the empty string
df2 = df.Courses.replace[np.nan,'',regex = True]
print[df2]
# Remove the nan and fill some values
df2 = df.Courses.replace[np.nan,'value',regex = True]
print[df2]
Now, let’s create a DataFrame with a few rows and columns and execute some examples and validate the results. Our DataFrame contains column names Courses, Fee, Duration and Discount.
import pandas as pd
import numpy as np
technologies = {
'Courses':["Spark",np.nan,"Hadoop","Python","pandas",np.nan,"Java"],
'Fee' :[20000,25000, np.nan,22000,24000,np.nan,22000],
'Duration':[np.nan,'40days','35days', np.nan,'60days','50days','55days'],
'Discount':[1000,np.nan,1500,np.nan,2500,2100,np.nan]
}
df = pd.DataFrame[technologies]
print[df]
Yields below output.
Courses Fee Duration Discount
0 Spark 20000.0 NaN 1000.0
1 NaN 25000.0 40days NaN
2 Hadoop NaN 35days 1500.0
3 Python 22000.0 NaN NaN
4 pandas 24000.0 60days 2500.0
5 NaN NaN 50days 2100.0
6 Java 22000.0 55days NaN
2. Convert Nan to Empty String in Pandas
Use df.replace[np.nan,'',regex=True] method to replace all NaN values to an empty string in the Pandas DataFrame column.
# All DataFrame replace empty string
df2 = df.replace[np.nan, '', regex=True]
print[df2]
Yields below output.
Courses Fee Duration Discount
0 Spark 20000.0 1000.0
1 25000.0 40days
2 Hadoop 35days 1500.0
3 Python 22000.0
4 pandas 24000.0 60days 2500.0
5 50days 2100.0
6 Java 22000.0 55days
3. Multiple Columns Replace Empty String
In order to replace NaN values with Blank strings on multiple columns or all columns from a list, use df[['Courses','Fee']] = df[['Courses','Fee']].fillna['']. This replaces NaN values on Courses and Fee column.
# Using multiple columns
df2 = df[['Courses','Fee' ]] = df[['Courses','Fee' ]].fillna['']
print[df2]
Yields below output.
Courses Fee
0 Spark 20000.0
1 25000.0
2 Hadoop
3 Python 22000.0
4 pandas 24000.0
5
6 Java 22000.0
4. Using fillna[] to NaN/Null Values With Empty String
Use pandas.DataFrmae.fillna[] to Replace NaN/Null values with an empty string. This replaces each NaN in pandas DataFrame with an empty string.
# Using pandas.DataFrame.fillna[] to nan values
df2 = df.fillna[""]
print[df2]
Yields below output.
Courses Fee Duration Discount
0 Spark 20000.0 1000.0
1 25000.0 40days
2 Hadoop 35days 1500.0
3 Python 22000.0
4 pandas 24000.0 60days 2500.0
5 50days 2100.0
6 Java 22000.0 55days
5. fillna[] with inplace=True
If you notice the above output after applying fillna[] function, it returns a new DataFrame, In order
to update the current/referring DataFrame in place use df.fillna['',inplace=True]. When using this, fillna[] method returns None type.
# Using pandas replace nan with null
df2 = df.fillna['', inplace=True]
print[df2]
Yields below output.
None
6. Replacing NaN with Empty String on a Specific Column
If you want to fill a single column, you can use df.Courses.fillna[''].
# Pandas single column using replace nan empty string
df2 = df.Courses.fillna['']
print[df2]
Yields below output.
0 Spark
1
2 Hadoop
3 Python
4 pandas
5
6 Java
Name: Courses, dtype: object
7. Replace NaN with Zeros
These examples replace NaN values with zeroes in a column.
# Using Courses column replace nan with Zeros
df2 = df['Courses']=df['Courses'].fillna[0]
print[df2]
# Using Discount column to replace nan with Zeros
df2 = df['Discount']=df['Discount'].fillna[0]
print[df2]
Yields below output.
0 Spark
1 0
2 Hadoop
3 Python
4 pandas
5 0
6 Java
Name: Courses, dtype: object
8. Remove the NaN and Fill the Empty String
Use df.Courses.replace[np.nan,'',regex=True] to remove the NaN and fill the empty string on a Courses column.
# Remove the nan and fill the empty string
df2 = df.Courses.replace[np.nan,'',regex = True]
print[df2]
Yields below output.
0 Spark
1
2 Hadoop
3 Python
4 pandas
5
6 Java
Name: Courses, dtype: object
9. Remove the NaN and Fill some Values
Use df.Courses.replace[np.nan,'value',regex=True] to remove the NaN and fill Value.
# Remove the nan and fill some values
df2 = df.Courses.replace[np.nan,'value',regex = True]
print[df2]
Yields below output.
0 Spark
1 value
2 Hadoop
3 Python
4 pandas
5 value
6 Java
Name: Courses, dtype: object
Conclusion
In this article, you have learned how to replace NaN with blank/empty
strings in Pandas using DataFrame.fillna[], DataFrame.replace[] functions, you have also learned how to replace single and multiple columns.
Happy Learning !!
You May Also Like
- How to Check If a Value is NaN in a Pandas DataFrame
- Combine Two Columns of Text in Pandas DataFrame
- How to Drop Rows with NaN Values in Pandas DataFrame
- Add an Empty Column to a Pandas DataFrame
- Pandas Select DataFrame Columns by Label or Index
References
- //pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html