By using replace() or fillna() methods you can replace NaN values with Blank/Empty string in Pandas DataFrame. NaN stands for Not A Nuber 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 pandas 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.
1. Quick Examples of Replace NaN to Empty/Blank String
If you are in a hurry, below are some quick examples of how to replace NaN with a blank/empty string in Pandas DataFrame.
# Below are the 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("Create DataFrame:\n", df)
Yields below output.
2. Convert Nan to Empty String in Pandas
Use df.replace(np.nan,'',regex=True) method to replace all NaN values with an empty string in the Pandas DataFrame column.
# All DataFrame replace empty string
df2 = df.replace(np.nan, '', regex=True)
print("After replacing the NaN values with an empty string:\n", df2)
Yields below output.
Related: You can also replace an empty/blank string with NaN values.
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("After replacing the NaN values with an empty string:\n", df2)
Yields below output.
# Output:
# After replacing the NaN values with an empty string:
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.DataFrame.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("After replacing the NaN values with an empty string:\n", df2)
Yields below output.
# Output:
# After replacing the NaN values with an empty string:
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("After replacing the NaN values with an empty string:\n", df2)
Yields below output.
# 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("After replacing the NaN values with an empty string:\n", df2)
Yields below output.
# Output:
# After replacing the NaN values with an empty string:
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("After replacing the NaN values with zeros:\n", df2)
# Using Discount column to replace nan with Zeros
df2 = df['Discount']=df['Discount'].fillna(0)
print("After replacing the NaN values with zeros:\n", df2)
Yields below output.
# Output:
# After replacing the NaN values with zeros:
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("After replacing NaN values with an empty string:\n", df2)
Yields below output.
# Output:
# After replacing NaN values with an empty string:
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("After replacing NaN values with specified value:\n", df2)
Yields below output.
# Output:
# After replacing NaN values with specified value:
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 !!
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