Pandas DataFrame provides a number of powerful features to manipulate tabular data. While working with your data, it may happen that there are NaNs present in it. NaNs are used as a placeholder for missing data and it’s better (and in a lot of cases required) to treat these NaNs before you proceed to your next steps. One of the ways to do it is to simply remove the rows that contain such values. In this tutorial we’ll look at how to drop rows with NaN values in a pandas dataframe using the `dropna()`

function.

## Pandas `dropna()`

function

The pandas dataframe function dropna() is used to remove missing values from a dataframe. It drops rows by default (as `axis`

is set to `0`

by default) and can be used in a number of use-cases (discussed below). The following is the syntax:

`df.dropna()`

It returns a dataframe with the NA entries dropped. To modify the dataframe in-place pass `inplace=True`

Examples of some of the use cases are –

### 1. Drop rows with any NA value

By default, the `dropna()`

function drops rows containing **any** NA value.

```
import numpy as np
np.random.seed(0)
import pandas as pd
# create a sample dataframe
df = pd.DataFrame(np.random.randint(1,9, (6,3)), columns=['A', 'B', 'C'])
df.iloc[::2,0] = np.nan
df.iloc[::3,1] = np.nan
df.iloc[::4,2] = np.nan
# print the dataframe
print("Before dropping rows:\n", df)
# drop rows with NaNs
df_dropped = df.dropna()
print("\nAfter dropping rows:\n", df_dropped)
```

Output:

```
Before dropping rows:
A B C
0 NaN NaN NaN
1 1.0 4.0 4.0
2 NaN 8.0 2.0
3 4.0 NaN 3.0
4 NaN 8.0 NaN
5 1.0 1.0 5.0
After dropping rows:
A B C
1 1.0 4.0 4.0
5 1.0 1.0 5.0
```

In the above example, you can see that using `dropna()`

with default parameters resulted in all the rows that contained any NA values getting dropped. This behavior of the dropna() function is controlled by the parameter `how`

which takes either `'any'`

or `'all'`

and is `'any'`

by default as was the case in the above example.

### 2. Drop rows only if all columns are NA

```
import numpy as np
np.random.seed(0)
import pandas as pd
# create a sample dataframe
df = pd.DataFrame(np.random.randint(1,9, (6,3)), columns=['A', 'B', 'C'])
df.iloc[::2,0] = np.nan
df.iloc[::3,1] = np.nan
df.iloc[::4,2] = np.nan
# print the dataframe
print("Before dropping rows:\n", df)
# drop rows with NaNs
df_dropped = df.dropna(how='all')
print("\nAfter dropping rows:\n", df_dropped)
```

Output:

**Data Science Programs By Skill Level**

**Introductory** ⭐

- Harvard University Data Science: Learn R Basics for Data Science
- Standford University Data Science: Introduction to Machine Learning
- UC Davis Data Science: Learn SQL Basics for Data Science
- IBM Data Science: Professional Certificate in Data Science
- IBM Data Analysis: Professional Certificate in Data Analytics
- Google Data Analysis: Professional Certificate in Data Analytics
- IBM Data Science: Professional Certificate in Python Data Science
- IBM Data Engineering Fundamentals: Python Basics for Data Science

**Intermediate ⭐⭐⭐**

- Harvard University Learning Python for Data Science: Introduction to Data Science with Python
- Harvard University Computer Science Courses: Using Python for Research
- IBM Python Data Science: Visualizing Data with Python
- DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization

**Advanced ⭐⭐⭐⭐⭐**

- UC San Diego Data Science: Python for Data Science
- UC San Diego Data Science: Probability and Statistics in Data Science using Python
- Google Data Analysis: Professional Certificate in Advanced Data Analytics
- MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
- MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science

**🔎 Find Data Science Programs 👨💻 111,889 already enrolled**

Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.

```
Before dropping rows:
A B C
0 NaN NaN NaN
1 1.0 4.0 4.0
2 NaN 8.0 2.0
3 4.0 NaN 3.0
4 NaN 8.0 NaN
5 1.0 1.0 5.0
After dropping rows:
A B C
1 1.0 4.0 4.0
2 NaN 8.0 2.0
3 4.0 NaN 3.0
4 NaN 8.0 NaN
5 1.0 1.0 5.0
```

In the above example you can see that only the row which had all columns as `NaN`

was dropped. This was because the argument `how=all`

was passed to the `dropna()`

function.

### 2. Drop rows with a certain minimum number of NAs

```
import numpy as np
np.random.seed(0)
import pandas as pd
# create a sample dataframe
df = pd.DataFrame(np.random.randint(1,9, (6,3)), columns=['A', 'B', 'C'])
df.iloc[::2,0] = np.nan
df.iloc[::3,1] = np.nan
df.iloc[::4,2] = np.nan
# print the dataframe
print("Before dropping rows:\n", df)
# drop rows with NaNs
df_dropped = df.dropna(thresh=2)
print("\nAfter dropping rows:\n", df_dropped)
```

Output:

```
Before dropping rows:
A B C
0 NaN NaN NaN
1 1.0 4.0 4.0
2 NaN 8.0 2.0
3 4.0 NaN 3.0
4 NaN 8.0 NaN
5 1.0 1.0 5.0
After dropping rows:
A B C
1 1.0 4.0 4.0
2 NaN 8.0 2.0
3 4.0 NaN 3.0
5 1.0 1.0 5.0
```

You can also drop rows that have a certain minimum number of NAs by passing a threshold to the `dropna()`

function. In the above example, we pass the argument `thresh=2`

to drop only those rows that had 2 or more NaNs.

### 4. Drop rows only if NAs are present in specific column(s)

```
import numpy as np
np.random.seed(0)
import pandas as pd
# create a sample dataframe
df = pd.DataFrame(np.random.randint(1,9, (6,3)), columns=['A', 'B', 'C'])
df.iloc[::2,0] = np.nan
df.iloc[::3,1] = np.nan
df.iloc[::4,2] = np.nan
# print the dataframe
print("Before dropping rows:\n", df)
# drop rows with NaNs
df_dropped = df.dropna(subset=['B'])
print("\nAfter dropping rows:\n", df_dropped)
```

Output:

```
Before dropping rows:
A B C
0 NaN NaN NaN
1 1.0 4.0 4.0
2 NaN 8.0 2.0
3 4.0 NaN 3.0
4 NaN 8.0 NaN
5 1.0 1.0 5.0
After dropping rows:
A B C
1 1.0 4.0 4.0
2 NaN 8.0 2.0
4 NaN 8.0 NaN
5 1.0 1.0 5.0
```

You can use `dropna()`

such that it drops rows only if NAs are present in certain column(s). You can pass the columns to check for as a list to the `subset`

parameter. In the above example, we drop only the rows that had column `B`

as `NaN`

.

For more on the `dropna()`

function check out its official documentation.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5 and pandas version 1.0.5

More on Pandas DataFrames –

- Pandas – Sort a DataFrame
- Change Order of Columns of a Pandas DataFrame
- Pandas DataFrame to a List in Python
- Pandas – Count of Unique Values in Each Column
- Pandas – Replace Values in a DataFrame
- Pandas – Filter DataFrame for multiple conditions
- Pandas – Random Sample of Rows
- Pandas – Random Sample of Columns
- Save Pandas DataFrame to a CSV file
- Pandas – Save DataFrame to an Excel file
- Create a Pandas DataFrame from Dictionary
- Convert Pandas DataFrame to a Dictionary
- Drop Duplicates from a Pandas DataFrame
- Concat DataFrames in Pandas
- Append Rows to a Pandas DataFrame
- Compare Two DataFrames for Equality in Pandas
- Get Column Names as List in Pandas DataFrame
- Select One or More Columns in Pandas
- Pandas – Rename Column Names
- Pandas – Drop one or more Columns from a Dataframe
- Pandas – Iterate over Rows of a Dataframe
- How to Reset Index of a Pandas DataFrame?
- Read CSV files using Pandas – With Examples
- Apply a Function to a Pandas DataFrame

**Subscribe to our newsletter for more informative guides and tutorials. ****We do not spam and you can opt out any time.**