Title: Improve error message for missing function parameters
State: closed

The error message in the fingerprint module was missing the f-string 'f' symbol, so the error message returned by fingerprint.py, line 469 was literally "function {func} is missing parameters {fingerprint_names} in signature."

This has been fixed.

Comments: _The documentation is not available anymore as the PR was closed or merged._CI errors are unrelated<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006681 / 0.011353 (-0.004672) | 0.004132 / 0.011008 (-0.006876) | 0.085045 / 0.038508 (0.046536) | 0.077680 / 0.023109 (0.054571) | 0.382042 / 0.275898 (0.106144) | 0.412932 / 0.323480 (0.089452) | 0.005339 / 0.007986 (-0.002646) | 0.003408 / 0.004328 (-0.000921) | 0.065280 / 0.004250 (0.061030) | 0.055732 / 0.037052 (0.018680) | 0.400231 / 0.258489 (0.141742) | 0.432497 / 0.293841 (0.138656) | 0.031532 / 0.128546 (-0.097014) | 0.008721 / 0.075646 (-0.066925) | 0.289612 / 0.419271 (-0.129660) | 0.053089 / 0.043533 (0.009556) | 0.383300 / 0.255139 (0.128161) | 0.401204 / 0.283200 (0.118004) | 0.023582 / 0.141683 (-0.118100) | 1.493854 / 1.452155 (0.041699) | 1.583497 / 1.492716 (0.090781) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.239163 / 0.018006 (0.221157) | 0.469555 / 0.000490 (0.469065) | 0.008325 / 0.000200 (0.008125) | 0.000113 / 0.000054 (0.000059) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.028975 / 0.037411 (-0.008436) | 0.084195 / 0.014526 (0.069669) | 0.189394 / 0.176557 (0.012837) | 0.158010 / 0.737135 (-0.579125) | 0.097502 / 0.296338 (-0.198837) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.383085 / 0.215209 (0.167876) | 3.827030 / 2.077655 (1.749375) | 1.872279 / 1.504120 (0.368159) | 1.705808 / 1.541195 (0.164613) | 1.833706 / 1.468490 (0.365216) | 0.484744 / 4.584777 (-4.100033) | 3.658221 / 3.745712 (-0.087491) | 3.398462 / 5.269862 (-1.871399) | 2.064974 / 4.565676 (-2.500703) | 0.057740 / 0.424275 (-0.366535) | 0.007926 / 0.007607 (0.000319) | 0.465358 / 0.226044 (0.239314) | 4.652951 / 2.268929 (2.384022) | 2.328390 / 55.444624 (-53.116235) | 2.000606 / 6.876477 (-4.875870) | 2.268391 / 2.142072 (0.126318) | 0.586537 / 4.805227 (-4.218690) | 0.134749 / 6.500664 (-6.365915) | 0.061276 / 0.075469 (-0.014193) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.337913 / 1.841788 (-0.503875) | 20.232122 / 8.074308 (12.157814) | 14.478579 / 10.191392 (4.287187) | 0.167545 / 0.680424 (-0.512878) | 0.018745 / 0.534201 (-0.515456) | 0.401209 / 0.579283 (-0.178074) | 0.425748 / 0.434364 (-0.008616) | 0.462539 / 0.540337 (-0.077798) | 0.652446 / 1.386936 (-0.734490) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007159 / 0.011353 (-0.004194) | 0.004091 / 0.011008 (-0.006917) | 0.066202 / 0.038508 (0.027694) | 0.083096 / 0.023109 (0.059987) | 0.402160 / 0.275898 (0.126261) | 0.440565 / 0.323480 (0.117085) | 0.005757 / 0.007986 (-0.002228) | 0.003445 / 0.004328 (-0.000884) | 0.065498 / 0.004250 (0.061248) | 0.059787 / 0.037052 (0.022735) | 0.407017 / 0.258489 (0.148528) | 0.448270 / 0.293841 (0.154429) | 0.033606 / 0.128546 (-0.094941) | 0.008744 / 0.075646 (-0.066902) | 0.072902 / 0.419271 (-0.346369) | 0.050144 / 0.043533 (0.006611) | 0.401069 / 0.255139 (0.145930) | 0.426389 / 0.283200 (0.143189) | 0.023297 / 0.141683 (-0.118386) | 1.506152 / 1.452155 (0.053998) | 1.570211 / 1.492716 (0.077495) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.235759 / 0.018006 (0.217753) | 0.488410 / 0.000490 (0.487921) | 0.004587 / 0.000200 (0.004387) | 0.000115 / 0.000054 (0.000060) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034123 / 0.037411 (-0.003289) | 0.102163 / 0.014526 (0.087638) | 0.110892 / 0.176557 (-0.065664) | 0.166000 / 0.737135 (-0.571135) | 0.110845 / 0.296338 (-0.185494) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.431397 / 0.215209 (0.216188) | 4.291540 / 2.077655 (2.213885) | 2.298248 / 1.504120 (0.794128) | 2.134752 / 1.541195 (0.593557) | 2.207913 / 1.468490 (0.739423) | 0.490607 / 4.584777 (-4.094170) | 3.683078 / 3.745712 (-0.062635) | 3.314266 / 5.269862 (-1.955596) | 2.059488 / 4.565676 (-2.506188) | 0.057876 / 0.424275 (-0.366399) | 0.007696 / 0.007607 (0.000089) | 0.512186 / 0.226044 (0.286142) | 5.124071 / 2.268929 (2.855142) | 2.803913 / 55.444624 (-52.640711) | 2.428558 / 6.876477 (-4.447919) | 2.655207 / 2.142072 (0.513135) | 0.584589 / 4.805227 (-4.220638) | 0.133518 / 6.500664 (-6.367146) | 0.060729 / 0.075469 (-0.014740) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.352916 / 1.841788 (-0.488872) | 20.249632 / 8.074308 (12.175323) | 15.283079 / 10.191392 (5.091686) | 0.157601 / 0.680424 (-0.522823) | 0.019650 / 0.534201 (-0.514551) | 0.396398 / 0.579283 (-0.182885) | 0.430111 / 0.434364 (-0.004252) | 0.480627 / 0.540337 (-0.059710) | 0.642165 / 1.386936 (-0.744771) |

</details>
</details>

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