I am trying to write a Pandas dataframe (or can use a numpy array) to a mysql database using MysqlDB . MysqlDB doesn't seem understand 'nan' and my database throws out an error saying nan is not in the field list. I need to find a way to convert the 'nan' into a NoneType. Any ideas?

Replacing Pandas or Numpy Nan with a None to use with MysqlDB

Answers (19)

Reputation: 7068

df = df.replace({np.nan: None})

Note: For pandas versions <1.4, this changes the dtype of all affected columns to object.
To avoid that, use this syntax instead:

df = df.replace(np.nan, None)

Note 2: If you don't want to import numpy, np.nan can be replaced with native float('nan'):

df = df.replace({float('nan'): None})

Credit goes to this guy here on this Github issue, Killian Huyghe's comment and Matt's answer.

Upvotes: 383

Matt

Reputation: 1284

Cleanest way to do this without importing anything extra like numpy is to create a nan object natively:

df = df.replace({float('nan'): None})

Upvotes: 1

Andy Hayden

Reputation: 375405

For pandas > 1.3.0 see this answer.

@bogatron has it right, you can use where, it's worth noting that you can do this natively in pandas:

df1 = df.where(pd.notnull(df), None)

Note: this changes the dtype of all columns to object.

Example:

In [1]: df = pd.DataFrame([1, np.nan])

In [2]: df
Out[2]: 
    0
0   1
1 NaN

In [3]: df1 = df.where(pd.notnull(df), None)

In [4]: df1
Out[4]: 
      0
0     1
1  None

Note: what you cannot do recast the DataFrames dtype to allow all datatypes types, using astype, and then the DataFrame fillna method:

df1 = df.astype(object).replace(np.nan, 'None')

Unfortunately neither this, nor using replace, works with None see this (closed) issue.

As an aside, it's worth noting that for most use cases you don't need to replace NaN with None, see this question about the difference between NaN and None in pandas.

However, in this specific case it seems you do (at least at the time of this answer).

Upvotes: 335

Max Segal

Reputation: 2045

replace np.nan with None is accomplished differently across different version of pandas:

if version.parse(pd.__version__) >= version.parse('1.3.0'):
    df = df.replace({np.nan: None})
else:
    df = df.where(pd.notnull(df), None)

this solves the issue that for pandas versions <1.3.0, if the values in df are already None then df.replace({np.nan: None}) will toggle them back to np.nan and vice versa.

Upvotes: 14

Marcelo Brisac

Reputation: 1

This should work: df["column"]=df["column"].apply(lambda x: None if pd.isnull(x) else x)

Upvotes: 0

Hesam Nikzad Jamnani

Reputation: 33

Sometimes it is better to use this code. Note that np refers to the numpy:

df = df.fillna(np.nan).replace([np.nan], [None])

Upvotes: 3

Danilo Matrangolo Marano

Reputation: 942

Doing it by hand is the only way that is working for me right now.

This answare from @rodney cox worked for me in almost every case.

The following code set all columns to object data type and then replace any null value to None. Setting the column data type to object is crucial because it prevents pandas to change the type further.

for col in df.columns:
    df[col] = df[col].astype(object)
    df.loc[df[col].isnull(), col] = None

Warning: This solution is not eficient, because it process columns that might not have np.nan values.

Upvotes: 0

Berel Levy

Reputation: 121

Astoundingly, None of the previous answers worked for me, so I had to do it for each column.

for column in df.columns:
            df[column] = df[column].where(pd.notnull(df[column]), None)

Upvotes: 1

SLuck

Reputation: 601

Yet another option, that actually did the trick for me:

df = df.astype(object).replace(np.nan, None)

Upvotes: 0

Kevin

Reputation: 41

After finding that neither the recommended answer, nor the alternate suggested worked for my application after a Pandas update to 1.3.2 I settled for safety with a brute force approach:

buf = df.to_json(orient='records')
recs = json.loads(buf)

Upvotes: 1

Jumikru

Reputation: 21

Convert numpy NaN to pandas NA before replacing with the where statement:

df = df.replace(np.NaN, pd.NA).where(df.notnull(), None)

Upvotes: 1

wfolkerts

Reputation: 107

This worked for me:

df = df.fillna(0)

Upvotes: -3

jameshollisandrew

Reputation: 1331

Do you have a code block to review by chance?

Using .loc, pandas can access records based on logic conditions (filtering) and do action with them (when using =). Setting a .loc mask equal to some value will change the return array inplace (so be a touch careful here; I suggest test on a df copy prior to using in code block).

df.loc[df['SomeColumn'].isna(), 'SomeColumn'] = None

The outer function is df.loc[row_label, column_label] = None. We're going to use a boolean mask for row_label by using the .isna() method to find 'NoneType' values in our column SomeColumn.

We'll use the .isna() method to return a boolean array of rows/records in column SomeColumn as our row_label: df['SomeColumn'].isna(). It will isolate all rows where SomeColumn has any of the 'NoneType' items pandas checks for with the .isna() method.

We'll use the column_label both when masking the dataframe for the row_label, and to identify the column we want to act on for the .loc mask.

Finally, we set the .loc mask equal to None, so the rows/records returned are changed to None based on the masked index.

Below are links to pandas documentation regarding .loc & .isna().

References:
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isna.html

Upvotes: 0

LasseRegin

Reputation: 51

I believe the cleanest way would be to make use of the na_value argument in the pandas.DataFrame.to_numpy() method (docs):

na_value : Any, optional

The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

New in version 1.1.0.

You could e.g. convert to dictionaries with NaN's replaced by None using

columns = df.columns.tolist()
dicts_with_nan_replaced = [
    dict(zip(columns, x))
    for x in df.to_numpy(na_value=None)
]

Upvotes: 2

gaatjeniksaan

Reputation: 1431

Another addition: be careful when replacing multiples and converting the type of the column back from object to float. If you want to be certain that your None's won't flip back to np.NaN's apply @andy-hayden's suggestion with using pd.where. Illustration of how replace can still go 'wrong':

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: df = pd.DataFrame({"a": [1, np.NAN, np.inf]})

In [4]: df
Out[4]:
     a
0  1.0
1  NaN
2  inf

In [5]: df.replace({np.NAN: None})
Out[5]:
      a
0     1
1  None
2   inf

In [6]: df.replace({np.NAN: None, np.inf: None})
Out[6]:
     a
0  1.0
1  NaN
2  NaN

In [7]: df.where((pd.notnull(df)), None).replace({np.inf: None})
Out[7]:
     a
0  1.0
1  NaN
2  NaN

Upvotes: 9

YaOzI

Reputation: 17458

Just an addition to @Andy Hayden's answer:

Since DataFrame.mask is the opposite twin of DataFrame.where, they have the exactly same signature but with opposite meaning:

DataFrame.where is useful for Replacing values where the condition is False.
DataFrame.mask is used for Replacing values where the condition is True.

So in this question, using df.mask(df.isna(), other=None, inplace=True) might be more intuitive.

Upvotes: 8

rodney cox

Reputation: 271

After stumbling around, this worked for me:

df = df.astype(object).where(pd.notnull(df),None)

Upvotes: 27

redacted

Reputation: 3959

Quite old, yet I stumbled upon the very same issue. Try doing this:

df['col_replaced'] = df['col_with_npnans'].apply(lambda x: None if np.isnan(x) else x)

Upvotes: 4

bogatron

Reputation: 19169

You can replace nan with None in your numpy array:

>>> x = np.array([1, np.nan, 3])
>>> y = np.where(np.isnan(x), None, x)
>>> print y
[1.0 None 3.0]
>>> print type(y[1])
<type 'NoneType'>

Upvotes: 28

Replacing Pandas or Numpy Nan with a None to use with MysqlDB

Answers (19)

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