Featured Post
How to Deal With Missing Data: Pandas Fillna() and Dropna()
- Get link
- Other Apps
Here are the best examples of Pandas fillna(), dropna() and sum() methods. We have explained the process in two steps - Counting and Replacing the Null values.
Count Nulls
## count null values column-wise
null_counts = df.isnull().sum()
print(null_counts)
```
Output:
```
Column1 1
Column2 1
Column3 5
dtype: int64
```
In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise:
```
df.isnull().sum()
```
##Code snippet to count null values row-wise:
```
df.isnull().sum(axis=1)
```
In the above code, `df` is the Pandas DataFrame for which you want to count the null values. The `isnull()` function returns a DataFrame with the same shape as `df`, where each element is a boolean value indicating whether that element is null or not.
The `sum()` function is then applied to the resulting DataFrame to count the number of null values.
Fill null values with zeros in Pandas
```
import pandas as pd
# create a sample dataframe
data = {'Column1': [1, 2, 3, 4, None],
'Column2': ['A', 'B', None, 'C', 'D'],
'Column3': [None, None, None, None, None]}
df = pd.DataFrame(data)
Fill Nulls
To fill null values with '0' in Pandas DataFrame, you can use the `fillna()` function. Here's an example code snippet to do this:
```
import pandas as pd
# create a sample dataframe
data = {'Column1': [1, 2, 3, 4, None],
'Column2': ['A', 'B', None, 'C', 'D'],
'Column3': [None, None, None, None, None]}
df = pd.DataFrame(data)
# fill null values with 0
df.fillna(0, inplace=True)
print(df)
```
Output:
```
Column1 Column2 Column3
0 1.0 A 0.0
1 2.0 B 0.0
2 3.0 0 0.0
3 4.0 C 0.0
4 0.0 D 0.0
```
In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then we use the `fillna()` function to replace all null values in the DataFrame with '0'. The `inplace=True` parameter ensures that the original DataFrame is modified and not a copy. Finally, we print the modified DataFrame with null values filled with '0'.
Note that the `axis` parameter is set to 0 by default in the `sum()` function, which means that it counts null values column-wise. To count null values row-wise, you need to set `axis` to 1.
Comments
Post a Comment
Thanks for your message. We will get back you.