Unpin pandas version

[angela97lin]

Closes #1618

This change will break our tests that use an older version aka break our conda package build 📦 🤯

A lot of the test changes have to do with how pandas now handles values returned in value_counts(). Now, if two values occur the same number of times, it is stored in the order that it was in the original data. Ex: [5, 1, 6, 5, 1] will be [5, 1, 6] :D

RE prediction explanation tests: confirmed by checking the transformed cols from the OHE that this is the reason for the changes. The new columns are:

Index(['amount', 'provider_American Express',
       'provider_Diners Club / Carte Blanche', 'provider_Discover',
       'provider_JCB 15 digit', 'provider_JCB 16 digit', 'provider_Maestro',
       'provider_Mastercard', 'provider_VISA 13 digit',
       'provider_VISA 16 digit', 'provider_VISA 19 digit', 'currency_CNY',
       'currency_EGP', 'currency_HTG', 'currency_IMP', 'currency_LAK',
       'currency_NAD', 'currency_PAB', 'currency_QAR', 'currency_TZS',
       'currency_XDR'],

Notably, currency_NAD, currency_HTG, and currency_PAB exist.

Using pandas 1.1.5, we get:

Index(['amount', 'provider_American Express',
       'provider_Diners Club / Carte Blanche', 'provider_Discover',
       'provider_JCB 15 digit', 'provider_JCB 16 digit', 'provider_Maestro',
       'provider_Mastercard', 'provider_VISA 13 digit',
       'provider_VISA 16 digit', 'provider_VISA 19 digit', 'currency_CNY',
       'currency_EGP', 'currency_IMP', 'currency_LAK', 'currency_MOP',
       'currency_MUR', 'currency_NIS', 'currency_QAR', 'currency_TZS',
       'currency_XDR'],
      dtype='object')

This aligns with the reason why the tests failed, as the columns chosen for the OHE have changed (due to behavior of value_counts changing)

[codecov]

Codecov Report

Merging #1708 (675e17f) into main (888dce8) will not change coverage.
The diff coverage is 100.0%.

@@           Coverage Diff           @@
##             main    #1708   +/-   ##
=======================================
  Coverage   100.0%   100.0%           
=======================================
  Files         273      273           
  Lines       22356    22356           
=======================================
  Hits        22350    22350           
  Misses          6        6           

Impacted Files	Coverage Δ
...components/transformers/encoders/onehot_encoder.py	100.0% <ø> (ø)
...rmers/preprocessing/delayed_feature_transformer.py	100.0% <ø> (ø)
...ta_checks_tests/test_class_imbalance_data_check.py	100.0% <ø> (ø)
...ta_checks_tests/test_invalid_targets_data_check.py	100.0% <ø> (ø)
.../tests/pipeline_tests/test_time_series_pipeline.py	100.0% <ø> (ø)
evalml/objectives/objective_base.py	100.0% <100.0%> (ø)
evalml/tests/automl_tests/test_automl.py	100.0% <100.0%> (ø)
evalml/tests/data_checks_tests/test_data_checks.py	100.0% <100.0%> (ø)
...s/prediction_explanations_tests/test_explainers.py	100.0% <100.0%> (ø)
...del_understanding_tests/test_partial_dependence.py	100.0% <100.0%> (ø)
... and 2 more

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[bchen1116]

If I'm understanding this correctly, pandas' value_counts method now first orders them by frequency (in descending order), then to break ties, it takes the order of occurrence into account?

Does that mean in your example in the description, currency_NAD, currency_HTG, and currency_PAB occur as frequently as currency_MOP, currency_MUR, currency_NIS in the new encoding, but that the first 3 occur AFTER the second 3 in the original input data?

Just wanted to check I am understanding this properly.

[angela97lin]

@bchen1116 Correct! value_counts always sorted by frequency first but now breaks ties based on their occurrence in the original data, so the test cases that required updates are when the frequencies were equivalent.

[bchen1116]

@angela97lin got it! looks good to me then!