How to Read a CSV File from Amazon S3 Using Python (With Headers and Rows Displayed)

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 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.
```
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)
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.
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