>Want to join two dataframes together like you'd join two database tables? df.join(other=df2, on='some_column') does the wrong thing, silently, what you really wanted was df.merge(right=df2, on='some_column')

Simply a matter of default type of join - join defaults to left while merge defaults to inner. They use the exact same internal join logic.

>What if they're optional? pd.DataFrame({'foo': [1,2,3,None]}) will silently change your integers to floating point values.

This was a long standing issue but is no longer true.

>Want to check if a dataframe is empty? Unlike lists or dicts, trying to turn a dataframe into a truth value will throw ValueError.

Those are 1D types where that's simple to reason about. It's not as straightforward in higher dimensions (what's the truth value of a (0, N) array?), which is why .empty exists

> Simply a matter of default type of join - join defaults to left while merge defaults to inner.

No, join does an index merge. For example, if you try to join with string keys, it'll throw an error (because strings and numeric indexes aren't compatible).

  left = pd.DataFrame({"abcd": ["a", "b", "c", "d"], "something": [1,2,3,4]})
  right = pd.DataFrame({"abcd": ["d", "c", "a", "b"], "something_else": [4,3,1,2]})
  left.join(other=right, on="abcd")
  
  ValueError: You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat

If you try to join with numeric keys:

  left = pd.DataFrame({"abcd": ["a", "b", "c", "d"], "something": [10,20,30,40]})
  right = pd.DataFrame({"abcd": ["d", "c", "a", "b"], "something": [40,30,10,20]})
  
  left.join(other=right, on="something", rsuffix="_r")
  
    abcd  something abcd_r  something_r
  0    a         10    NaN          NaN
  1    b         20    NaN          NaN
  2    c         30    NaN          NaN
  3    d         40    NaN          NaN

Or even worse if your numeric values are within the range for indexes, which kind of looks right if you're not paying attention:

  left = pd.DataFrame({"abcd": ["a", "b", "c", "d"], "something": [1,2,3,4]})
  right = pd.DataFrame({"abcd": ["d", "c", "a", "b"], "something": [4,3,1,2]})
  left.join(other=right, on="something", rsuffix="_r")
  
    abcd  something abcd_r  something_r
  0    a          1      c          3.0
  1    b          2      a          1.0
  2    c          3      b          2.0
  3    d          4    NaN          NaN

Whereas merge does what one would expect:

  left.merge(right=right, on="something", suffixes=['', '_r'])
  
    abcd  something abcd_r
  0    a         10      a
  1    b         20      b
  2    c         30      c
  3    d         40      d

>> What if they're optional? pd.DataFrame({'foo': [1,2,3,None]}) will silently change your integers to floating point values.

> This was a long standing issue but is no longer true.

Occurs in pandas 0.25.1 (and the release notes for 0.25.2 and 0.25.3 don't mention such a change), so that would likely be still the case in the latest stable release.

  pd.DataFrame({"foo": [1,2,3,4,None,9223372036854775807]})
  
              foo
  0  1.000000e+00
  1  2.000000e+00
  2  3.000000e+00
  3  4.000000e+00
  4           NaN
  5  9.223372e+18

It's also a lossy conversion if the integer values are large enough:

  df = pd.DataFrame({"foo": [1,2,3,4,None,9223372036854775807,9223372036854775806]})
  
              foo
  0  1.000000e+00
  1  2.000000e+00
  2  3.000000e+00
  3  4.000000e+00
  4           NaN
  5  9.223372e+18
  6  9.223372e+18
  
  df["foo"].unique()
  
  array([1.00000000e+00, 2.00000000e+00, 3.00000000e+00, 4.00000000e+00, nan, 9.22337204e+18])

>> Want to check if a dataframe is empty? Unlike lists or dicts, trying to turn a dataframe into a truth value will throw ValueError.

> Those are 1D types where that's simple to reason about. It's not as straightforward in higher dimensions (what's the truth value of a (0, N) array?), which is why .empty exists

It's not very pythonic, though. A definition of "all dimensions greater than 0" would've been much less surprising.

> Occurs in pandas 0.25.1 (and the release notes for 0.25.2 and 0.25.3 don't mention such a change), so that would likely be still the case in the latest stable release.

It was released in 0.24.0: https://pandas.pydata.org/pandas-docs/stable/user_guide/inte...

For example:

    pd.DataFrame({"foo": [1,2,3,4,None]}, dtype=pd.Int64Dtype())

        foo
    0     1
    1     2
    2     3
    3     4
    4  <NA>

    pd.DataFrame({"foo": [1,2,3,4,None,9223372036854775807,9223372036854775806]}, dtype=pd.Int64Dtype())

                       foo
    0                    1
    1                    2
    2                    3
    3                    4
    4                 <NA>
    5  9223372036854775807
    6  9223372036854775806

Sure, if you specify the type. It's still a gotcha because the default behavior is to upcast to floating point unless the type is defined for every integer column of every data frame, which isn't very pythonic.

The example with the (incorrect) join above shows how even other operations can cause this type conversion.

Yes, there's a lot of existing code written assuming the old behavior. But most code has only a few ingestion points, so it's pretty simple to turn on.

If anyone else also wonders what does pandas.DataFrame.join do [1]:

> on : str, list of str, or array-like, optional

> Column or index level name(s) in the caller to join on the index in other, otherwise joins index-on-index.

> DataFrame.join always uses other’s index but we can use any column in df.

Interesting choices but luckily I have never been bit by this. Maybe because I first looked up on google on how to do join in pandas. Nowadays, I mostly use pd.merge though.

[1] https://pandas.pydata.org/pandas-docs/stable/reference/api/p...

Missing value treatment is one of the major improvements in the upcoming pandas 1.0, including Integer, boolean and string column types with proper missing value support.

So you prefer the word join and merge is tripping you up? It's not sql!