5 ms·

I've found Pandas extremely easy to learn and to use; to the point where I find it confusing to see somebody say that it's not human-understandable. If you're

I've found Pandas extremely easy to learn and to use; to the point where I find it confusing to see somebody say that it's not human-understandable. If you're reading this thread and wondering if it's easy to hard to use, I suggest taking a look at the docs (https://pandas.pydata.org/pandas-docs/stable/index.html https://pandas.pydata.org/pandas-docs/stable/index.html) and making your own decision. I find the combination of basic intros, user guides, and the API reference to be extremely usable and understandable; and I am reasonably sure I am human. But opinions may vary.

anakaine 7y ago
And I also. The other poster, in my opinion, has it backwards. Seeing how each option can be used in a myriad of examples is particularly helpful and saves me much time struggling to work out what the right method/ tool is, and how to use it.
jfim 7y ago
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 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') Got a list of integers that you want to put into a dataframe? pd.DataFrame({'foo': [1,2,3]}) will do what you want. What if they're optional? pd.DataFrame({'foo': [1,2,3,None]}) will silently change your integers to floating point values. Enjoy debugging your joins (sorry, merges) with large integer values. Want to check if a dataframe is empty? Unlike lists or dicts, trying to turn a dataframe into a truth value will throw ValueError.
- 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.
- 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!
teej 7y ago
You can’t pick up a dictionary and determine how difficult it is to speak a language. One of the most difficult things about pandas is knowing how to shape your data frame before you plug it in to something. Most of my frustration comes from finding the magic incantation of symbols that will reshape my data frame the right way.
Seufman 7y ago
Yeah, I agree: it's super straightforward. It's hard for me to understand how people could find it inscrutable / confusing. Pandas is a LIFESAVER for anyone who needs to manipulate datasets programmatically.
- mattrp 7y ago
  Big fan of pandas as well... but I have to admit there are some things at the beginning were like wtf... some of which are now deprecated thankfully. But more on topic to this post I really can’t see why someone would want to solve speed on small datasets by incorporating numpy into a new form of pandas... both projects are so established, why would you attach your project to some other dev who know has to keep pace with numpy and pandas improvements when you could just import pandas, import numpy and be done with it?
edgyquant 7y ago
I learned Python by using pandas so for awhile I was one of the devs you speak of. I can't imagine I'm alone.

anakaine 7y ago

And I also. The other poster, in my opinion, has it backwards. Seeing how each option can be used in a myriad of examples is particularly helpful and saves me much time struggling to work out what the right method/ tool is, and how to use it.

jfim 7y ago

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 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') Got a list of integers that you want to put into a dataframe? pd.DataFrame({'foo': [1,2,3]}) will do what you want. What if they're optional? pd.DataFrame({'foo': [1,2,3,None]}) will silently change your integers to floating point values. Enjoy debugging your joins (sorry, merges) with large integer values. Want to check if a dataframe is empty? Unlike lists or dicts, trying to turn a dataframe into a truth value will throw ValueError.

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.
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!

teej 7y ago

You can’t pick up a dictionary and determine how difficult it is to speak a language. One of the most difficult things about pandas is knowing how to shape your data frame before you plug it in to something. Most of my frustration comes from finding the magic incantation of symbols that will reshape my data frame the right way.

Seufman 7y ago

Yeah, I agree: it's super straightforward. It's hard for me to understand how people could find it inscrutable / confusing. Pandas is a LIFESAVER for anyone who needs to manipulate datasets programmatically.

mattrp 7y ago
Big fan of pandas as well... but I have to admit there are some things at the beginning were like wtf... some of which are now deprecated thankfully. But more on topic to this post I really can’t see why someone would want to solve speed on small datasets by incorporating numpy into a new form of pandas... both projects are so established, why would you attach your project to some other dev who know has to keep pace with numpy and pandas improvements when you could just import pandas, import numpy and be done with it?

edgyquant 7y ago

I learned Python by using pandas so for awhile I was one of the devs you speak of. I can't imagine I'm alone.