QST: How to solve pandas (2.2.0) "FutureWarning: Downcasting behavior in `replace` is deprecated" on a Series?

[buhtz]

QST: How to solve pandas (2.2.0) "FutureWarning: Downcasting behavior in `replace` is deprecated" on a Series?

[buhtz]

This is exactly the thing we are trying to solve. replace was previously casting your dtypes and will stop doing so in pandas 3

But it is unclear how to replace and cast. E.g. when I have [0, 1] integers they stand for female and male.

df.gender = df.gender.astype(str)
df.gender = df.gender.replace({'0': 'male', '1': 'female'})

Is that the solution you have in mind? From a users perspective it is a smelling workaround.

The other way around is nearly not possible because I can not cast a str word to an integer.

print(df.gender)  # ['male', 'male', 'female']
df.gender = df.gender.astype(int)  # <-- ERROR
df.gender = df.gender.replace({'male': 0, 'female': 1})

What is wrong with casting in replace() ?

[caballerofelipe]

I got here, trying to understand what pd.set_option('future.no_silent_downcasting', True) does.

The message I get is from .fillna(), which is the same message for .ffill() and .bfill(). So I'm posting this here in case someone is looking for the same answer using the mentioned functions. This is the warning message I get:

FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version.
Call result.infer_objects(copy=False) instead.
To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`

Maybe the confusion arises from the way the message is phrased, I believe it's kind of confusing, it creates more questions than answers:

• Do I need to do some downcasting?
• Am I doing some downcasting somewhere where I am not aware?
• When the messages stated Call result.infer_objects(copy=False) instead., is it telling me to call it before the function I'm trying to use, after? Is it telling me not to use the function? (I guess not since infer_objects should do something different than replace or one of the fill functions)
• By using pd.set_option('future.no_silent_downcasting', True) am I removing the downcasting or am I making the downcasting not silent? Maybe both?

From what I understand, pd.set_option('future.no_silent_downcasting', True) removes the downcasting the functions do and if it needs to do some downcasting an error would be raised, but I would need to be corrected here if I'm wrong.

[phofl]

Just do

pandas.set_option("future.no_silent_downcasting", True)

as suggested on the stack overflow question

The series will retain object dtype in pandas 3.0 instead of casting to int64

[jerome-white]

I'm having this problem as well. I have the feeling it's related to .replace changing the types of the values (as one Stack Overflow commenter implied). Altering the original example slightly:

s = Series(['foo', 'bar'])
replace_dict = {'foo': '1', 'bar': '2'} # replacements maintain original types
s = s.replace(replace_dict)

makes the warning go away.

I agree with @buhtz in that setting the "future" option isn't really getting at the root of understanding how to make this right. I think the hard part for most of us who have relied on .replace is that we never thought of it as doing any casting -- it was replacing. Now the semantics seem to have changed. It'd be great to reopen this issue to clarify the thinking, intention, and direction so that we can come up with appropriate work-arounds.

[caballerofelipe]

So... I did some digging and I think I have a better grasp of what's going on with this FutureWarning. So I wrote an article in Medium to explain what's happening. If you want to give it a read, here it is:

Deciphering the cryptic FutureWarning for .fillna in Pandas 2

Long story short, do:

with pd.option_context('future.no_silent_downcasting', True):
    # Do you thing with fillna, ffill, bfill, replace... and possible use infer_objects if needed

[jerome-white]

I feel like this thread is starting to become a resource. In that spirit:

I just experienced another case where .replace would have been amazing, but I now need an alternative: a column of strings that are meant to be floats, where the only "offending" values are empty strings (meant to be NaN's). Consider:

records = [
    {'a': ''},
    {'a': 12.3},
]
df = pd.DataFrame.from_records(records)

I would have first reached for .replace. Now I consider .filla, but that doesn't work either. Using .assign with .to_numeric does the trick:

In [1]: df.dtypes
Out[1]: 
a    object
dtype: object

In [2]: x = df.assign(a=lambda x: pd.to_numeric(x['a']))

In [3]: x
Out[3]: 
      a
0   NaN
1  12.3

In [4]: x.dtypes
Out[4]: 
a    float64
dtype: object

[buhtz]

pandas.set_option("future.no_silent_downcasting", True)

But doesn't this just deactivate the message but doesn't modify the behavior.

To my understanding the behavior is the problem and need to get solved. Or not?
My intention is to extinguish the fire and not just turn off the fire alarm but let the house burn down.

[phofl]

s that we never thought of it as doing any casting

This is exactly the thing we are trying to solve. replace was previously casting your dtypes and will stop doing so in pandas 3

[jerome-white]

The other way around is nearly not possible because I can not cast a str word to an integer.

One alternative (although I realise a non .replace supported "alternative" may not be what was actually desired) is to use categoricals with .assign:

import pandas as pd

df = pd.DataFrame(['male', 'male', 'female'], columns=['gender']) # from the original example
genders = pd.Categorical(df['gender'])
df = df.assign(gender=genders.codes)

If semantically similar data is spread across multiple columns, it gets a little more involved:

import random
import numpy as np
import pandas as pd

def create_data(columns):
    genders = ['male', 'male', 'female']
    for i in columns:
        yield (i, genders.copy())
        random.shuffle(genders)

# Create the dataframe
columns = [ f'gender_{x}' for x in range(3) ]
df = pd.DataFrame(dict(create_data(columns)))

# Incorporate all relevant data into the categorical
view = (df
        .filter(items=columns)
	.unstack())
categories = pd.Categorical(view)
values = np.hsplit(categories.codes, len(columns))
to_replace = dict(zip(columns, values))

df = df.assign(**to_replace)

which I think is what the Categorical documentation is trying to imply.

[caballerofelipe]

From your code:

x = df.assign(a=lambda x: pd.to_numeric(x['a']))

I would do it like this, it feels a little cleaner and easier to read:

df['a'] = pd.to_numeric(df['a'])

---

You said you wanted to use replace, if you want to use it, you can do this:

with pd.option_context('future.no_silent_downcasting', True):
    df2 = (df
           .replace('', float('nan')) # Replace empty string for nans
           .infer_objects()           # Allow pandas to try to "infer better dtypes"
           )

df2.dtypes

# a    float64
# dtype: object

---

A note about

Now I consider .filla, but that doesn't work either.

That would not work because .fillna fills na values but '' (empty string) is not na. (see Filling missing data).