# Python Snippets: Dropping Infinite Values From Dataframes in Pandas

Infinite values can occur more often than people expect, especially for calculated data.

For example, in a recent post I calculated the Twitter Follower-Friend ratio by dividing the `followers_count`

series by the `friends_count`

series.
But what happens when `friends_count`

is zero?
`Inf`

.

In that particular case, I wanted to drop the rows.
Here’s how to do it: ^{1}

```
import pandas as pd
import numpy as np
# example dataframe
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [9, 0, 8, 0]})
df["c"] = df["a"] / df["b"]
df
```

a | b | c | |
---|---|---|---|

0 | 1 | 9 | 0.111111 |

1 | 2 | 0 | inf |

2 | 3 | 8 | 0.375000 |

3 | 4 | 0 | inf |

```
# replace inf with NaN then dropna
df.replace([np.inf, -np.inf], np.nan).dropna(subset=["c"], how="all")
```

a | b | c | |
---|---|---|---|

0 | 1 | 9 | 0.111111 |

2 | 3 | 8 | 0.375000 |

Mind you, this is only helpful if you want to discard rows with `inf`

values.
Otherwise, `df.replace()`

can be used to “fix” your values to something that makes sense for the application without discarding the row.