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Pandas has enough gotchas that it looks friendly until you hit one of them. Examples of gotchas: Want to join two dataframes together like you'd join two datab

• qwhelan 7y ago
  >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
  • jfim 7y ago
    > 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.
    • qwhelan 7y ago
      > 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/integer_na.html 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
      • jfim 7y ago
        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.
        • qwhelan 7y ago
          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.
• squaresmile 7y ago
  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/pandas.DataFrame.join.html https://pandas.pydata.org/pandas-docs/stable/reference/api/p...
• maxnoe 7y ago
  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.
• mcrad 7y ago
  So you prefer the word join and merge is tripping you up? It's not sql!