# Pandas dropna: Remove Rows with Missing Values in Python

> Remove rows with missing values from a Pandas DataFrame using dropna with how=all to drop fully empty rows or how=any to drop rows with any NaN values

**URL:** https://sentry.io/answers/remove-dataframe-rows-with-missing-values-in-python/

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## The Problem

In Pandas, how do I remove DataFrame rows that contain `None` or `NaN` across all columns? How can I do this when these values are present in only some columns?

## The Solution

We can achieve both of these results using the [`DataFrame.dropna`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html) method. For example:

```python
import pandas
from numpy import nan

df = pandas.DataFrame(
    {
        "Test 1": [90, 10, nan, nan],
        "Test 2": [41, nan, 32, nan],
        "Test 3": [89, 35, 72, nan],
        "Test 4": [52, nan, nan, nan],
    }
)
print(df)

# output:
#    Test 1  Test 2  Test 3  Test 4
# 0    90.0    41.0    89.0    52.0
# 1    10.0     NaN    35.0     NaN
# 2     NaN    32.0    72.0     NaN
# 3     NaN     NaN     NaN     NaN

df_no_empty_rows = df.dropna(how="all")  # drop rows containing all NaNs
print(df_no_empty_rows)

# output:
#    Test 1  Test 2  Test 3  Test 4
# 0    90.0    41.0    89.0    52.0
# 1    10.0     NaN    35.0     NaN
# 2     NaN    32.0    72.0     NaN

df_no_empty_values = df.dropna(how="any")  # drop rows containing any NaNs
print(df_no_empty_values)

# output:
#    Test 1  Test 2  Test 3  Test 4
# 0    90.0    41.0    89.0    52.0
```

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*Source: [sentry.io/answers/remove-dataframe-rows-with-missing-values-in-python/](https://sentry.io/answers/remove-dataframe-rows-with-missing-values-in-python/)*