diff --git a/dev_scripts_helpers/llms/llm_transform.py b/dev_scripts_helpers/llms/llm_transform.py index 369ed9b6a..cb3fde9e1 100755 --- a/dev_scripts_helpers/llms/llm_transform.py +++ b/dev_scripts_helpers/llms/llm_transform.py @@ -2,11 +2,11 @@ """ Read input from either stdin or a file, apply a specified transformation using -an LLM, and then write the output to either stdout or a file. It is -particularly useful for integrating with editors like Vim. +an LLM, and then write the output to either stdout or a file. It is particularly +useful for integrating with editors like Vim. -The script `dockerized_llm_transform.py` is executed within a Docker container to ensure -all dependencies are met. The Docker container is built dynamically if +The script `dockerized_llm_transform.py` is executed within a Docker container +to ensure all dependencies are met. The Docker container is built dynamically if necessary. The script requires an OpenAI API key to be set in the environment. Examples diff --git a/helpers/hpandas.py b/helpers/hpandas.py index 8a05d0d90..e48dcf9dd 100644 --- a/helpers/hpandas.py +++ b/helpers/hpandas.py @@ -2527,3 +2527,116 @@ def to_perc(vals: Union[List, pd.Series], **perc_kwargs: Dict[str, Any]) -> str: vals = pd.Series(vals) ret = hprint.perc(vals.sum(), len(vals), **perc_kwargs) return ret + + +# ############################################################################# + + +def convert_to_type(srs: pd.Series, type_: str) -> pd.Series: + """ + Convert the elements to a specific type when possible, and return nan otherwise. + + :param srs: column to convert + :param type_: type to convert to + :return: series with converted values or nan + """ + hdbg.dassert_isinstance(srs, pd.Series) + if type_ == "is_bool": + def is_bool(x: Any) -> bool: + # An element is a bool if it's a bool, or a string that can be converted + # to a bool, or an int / float that is 0 or 1. + true_values = ("True", "true", "1", 1, 1.0) + false_values = ("False", "false", "0", 0, 0.0) + if isinstance(x, bool): + ret = x + elif x in true_values: + ret = True + elif x in false_values: + ret = False + else: + ret = np.nan + return ret + ret = srs.map(lambda x: is_bool(x)) + elif type_ == "is_int": + ret = pd.to_numeric(srs, errors='coerce') + ret = ret.apply(lambda x: x if pd.notna(x) and float(x).is_integer() else np.nan) + elif type_ == "is_float": + ret = pd.to_numeric(srs, errors='coerce') + elif type_ == "is_string": + ret = srs.astype(str) + else: + # TODO(gp): Add timestamp. + raise ValueError(f"Unknown column type: {type_}") + return ret + + +def infer_column_types(srs: pd.Series) -> Tuple[Dict[str, float], pd.Series]: + """ + Infer the type of a column by trying to convert it to each type and picking + the one with the highest count, giving priority to stricter types. + + :param srs: column to infer the type of + :return: counts and values of the types, e.g., + ``` + counts = { + "is_bool": 0.2, + "is_int": 0.3, + "is_float": 0.5, + "is_string": 1.0 + } + ``` + """ + hdbg.dassert_isinstance(srs, pd.Series) + # Convert the column to each type. + vals = {} + types = ["is_bool", "is_int", "is_float", "is_string"] + for type_ in types: + vals[type_] = convert_to_type(srs, type_) + # Count the number of values for each type. + counts = {} + for type_ in types: + counts[type_] = float(vals[type_].notna().mean()) + # Choose the type with the highest count, using an order of preference. + if counts["is_bool"] > 0.0 and counts["is_bool"] >= max(counts["is_int"], counts["is_float"], counts["is_string"]): + type_ = "is_bool" + elif counts["is_int"] > 0.0 and counts["is_int"] >= max(counts["is_float"], counts["is_string"]): + type_ = "is_int" + elif counts["is_float"] > 0.0: # and counts["is_float"] >= counts["is_string"]: + type_ = "is_float" + else: + type_ = "is_string" + vals["type"] = type_ + # + return counts, vals[type_] + + +def infer_types_df(df: pd.DataFrame, *, report_invalid_values: bool = False, + log_level: int = logging.INFO, + ) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + Infer the type of each column in the DataFrame and return a new DataFrame + with the inferred types. + + :param df: DataFrame to infer the types of + :param report_invalid_values: whether to report invalid values + :return: tuple of (DataFrame with the inferred types, DataFrame with the counts) + """ + # Initialize the output DataFrame. + types_df = [] + df_out = df.copy() + # Process each column in the DataFrame. + for col_name in df.columns: + _LOG.info("Processing col_name='%s'", col_name) + counts, vals = infer_column_types(df[col_name]) + # If requested, print any values that failed conversion. + if report_invalid_values: + mask = vals.isna() + if mask.any(): + _LOG.log(log_level, "column=%s\n%s", col_name, df[col_name][mask]) + # Store results. + df_out[col_name] = vals + types_df.append(counts) + # Package the counts into a DataFrame. + types_df = pd.DataFrame(types_df) + types_df.index = df.columns + return df_out, types_df diff --git a/helpers/notebooks/hopenai_tutorial.ipynb b/helpers/notebooks/hopenai_tutorial.ipynb new file mode 100644 index 000000000..ff6fea50a --- /dev/null +++ b/helpers/notebooks/hopenai_tutorial.ipynb @@ -0,0 +1,12751 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CONTENTS:\n", + "- [Description](#description)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Description\n", + "\n", + "This notebook examines ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!sudo /bin/bash -c \"(source /venv/bin/activate; pip install --quiet jupyterlab-vim)\"\n", + "#!jupyter labextension enable" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-04-02T18:11:14.828251Z", + "start_time": "2021-04-02T18:11:14.514771Z" + } + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import logging\n", + "\n", + "import helpers.hdbg as hdbg\n", + "import helpers.henv as henv\n", + "import helpers.hprint as hprint" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2021-04-02T18:11:24.635995Z", + "start_time": "2021-04-02T18:11:18.239237Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# System signature\n", + " # Container version\n", + " container_version='1.2.0'\n", + " changelog_version='2.0.0'\n", + " # Git info\n", + " branch_name='CmampTask11862_Allow_dind_unit_tests_to_run_on_server_and_CI'\n", + " hash='0ca93d8c'\n", + " # Last commits:\n", + " * 0ca93d8c GP Saggese Merge ( 5 minutes ago) Fri May 9 22:09:03 2025 (HEAD -> CmampTask11862_Allow_dind_unit_tests_to_run_on_server_and_CI, origin/CmampTask11862_Allow_dind_unit_tests_to_run_on_server_and_CI)\n", + " |\\ \n", + " * | 99cbbf22 GP Saggese Lint ( 6 minutes ago) Fri May 9 22:08:07 2025 \n", + " | * 27b38c48 GP Saggese CmampTask12067_Read_docs_about_DataPull_4 (#698) ( 8 minutes ago) Fri May 9 22:06:25 2025 (origin/master, origin/HEAD, master)\n", + " # Platform info\n", + " system=Linux\n", + " node name=0f79e8b845ee\n", + " release=6.10.14-linuxkit\n", + " version=#1 SMP Thu Mar 20 16:32:56 UTC 2025\n", + " machine=aarch64\n", + " processor=aarch64\n", + " # psutils info\n", + " cpu count=8\n", + " cpu freq=None\n", + " memory=svmem(total=16749285376, available=14575529984, percent=13.0, used=1910644736, free=9673363456, active=2843516928, inactive=3252117504, buffers=490647552, cached=4674629632, shared=1093632, slab=694362112)\n", + " disk usage=sdiskusage(total=270233210880, used=102272610304, free=154199986176, percent=39.9)\n", + " # Docker info\n", + " has_docker=True\n", + " docker_version='28.0.4'\n", + " docker_needs_sudo=False\n", + " has_privileged_mode=True\n", + " is_inside_docker=True\n", + " has_docker_sibling_containers_support=True\n", + " has_docker_children_containers_support=True\n", + " # Packages\n", + " python: 3.12.3\n", + " cvxopt: ?\n", + " cvxpy: ?\n", + " gluonnlp: ?\n", + " gluonts: ?\n", + " joblib: 1.4.2\n", + " mxnet: ?\n", + " numpy: 2.2.3\n", + " pandas: 2.2.3\n", + " pyarrow: 19.0.1\n", + " scipy: 1.15.2\n", + " seaborn: 0.13.2\n", + " sklearn: 1.6.1\n", + " statsmodels: 0.14.4\n" + ] + } + ], + "source": [ + "print(henv.get_system_signature()[0])\n", + "\n", + "hprint.config_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2021-04-02T18:11:24.668793Z", + "start_time": "2021-04-02T18:11:24.638503Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0mWARNING: Running in Jupyter\n", + "INFO > cmd='/venv/lib/python3.12/site-packages/ipykernel_launcher.py -f /home/.local/share/jupyter/runtime/kernel-0f2f4a10-7f18-4858-af02-b60808101345.json'\n" + ] + } + ], + "source": [ + "# hdbg.init_logger(verbosity=logging.DEBUG)\n", + "hdbg.init_logger(verbosity=logging.INFO)\n", + "# hdbg.test_logger()\n", + "_LOG = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "!sudo /bin/bash -c \"(source /venv/bin/activate; pip install --quiet openai requests)\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import helpers.hopenai as hopenai" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "val = hopenai.get_model_stats()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'architecture': {'input_modalities': ['text', 'image'],\n", + " 'instruct_type': None,\n", + " 'modality': 'text+image->text',\n", + " 'output_modalities': ['text'],\n", + " 'tokenizer': 'Mistral'},\n", + " 'context_length': 131072,\n", + " 'created': 1746627341,\n", + " 'description': 'Mistral Medium 3 is a high-performance enterprise-grade '\n", + " 'language model designed to deliver frontier-level '\n", + " 'capabilities at significantly reduced operational cost. It '\n", + " 'balances state-of-the-art reasoning and multimodal '\n", + " 'performance with 8× lower cost compared to traditional large '\n", + " 'models, making it suitable for scalable deployments across '\n", + " 'professional and industrial use cases.\\n'\n", + " '\\n'\n", + " 'The model excels in domains such as coding, STEM reasoning, '\n", + " 'and enterprise adaptation. It supports hybrid, on-prem, and '\n", + " 'in-VPC deployments and is optimized for integration into '\n", + " 'custom workflows. Mistral Medium 3 offers competitive '\n", + " 'accuracy relative to larger models like Claude Sonnet '\n", + " '3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining '\n", + " 'broad compatibility across cloud environments.',\n", + " 'id': 'mistralai/mistral-medium-3',\n", + " 'name': 'Mistral: Mistral Medium 3',\n", + " 'per_request_limits': None,\n", + " 'pricing': {'completion': '0.000002',\n", + " 'image': '0',\n", + " 'internal_reasoning': '0',\n", + " 'prompt': '0.0000004',\n", + " 'request': '0',\n", + " 'web_search': '0'},\n", + " 'supported_parameters': ['tools',\n", + " 'tool_choice',\n", + " 'max_tokens',\n", + " 'temperature',\n", + " 'top_p',\n", + " 'stop',\n", + " 'frequency_penalty',\n", + " 'presence_penalty',\n", + " 'response_format',\n", + " 'structured_outputs',\n", + " 'seed'],\n", + " 'top_provider': {'context_length': 131072,\n", + " 'is_moderated': False,\n", + " 'max_completion_tokens': None}}\n" + ] + } + ], + "source": [ + "import pprint\n", + "pprint.pprint(val[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Normalize the nested JSON\n", + "df = pd.json_normalize(val, sep='_')\n", + "df.set_index(\"id\", inplace=True)\n", + "# View the resulting DataFrame\n", + "#print(df.T) # Transpose just for readable vertical inspection" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name Mistral: Mistral Medium 3\n", + "created 1746627341\n", + "description Mistral Medium 3 is a high-performance enterpr...\n", + "context_length 131072\n", + "per_request_limits None\n", + "supported_parameters [tools, tool_choice, max_tokens, temperature, ...\n", + "architecture_modality text+image->text\n", + "architecture_input_modalities [text, image]\n", + "architecture_output_modalities [text]\n", + "architecture_tokenizer Mistral\n", + "architecture_instruct_type None\n", + "pricing_prompt 0.0000004\n", + "pricing_completion 0.000002\n", + "pricing_request 0\n", + "pricing_image 0\n", + "pricing_web_search 0\n", + "pricing_internal_reasoning 0\n", + "top_provider_context_length 131072.0\n", + "top_provider_max_completion_tokens NaN\n", + "top_provider_is_moderated False\n", + "pricing_input_cache_read NaN\n", + "pricing_input_cache_write NaN\n", + "Name: mistralai/mistral-medium-3, dtype: object" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[0].T" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name object\n", + "created int64\n", + "description object\n", + "context_length int64\n", + "per_request_limits object\n", + "supported_parameters object\n", + "architecture_modality object\n", + "architecture_input_modalities object\n", + "architecture_output_modalities object\n", + "architecture_tokenizer object\n", + "architecture_instruct_type object\n", + "pricing_prompt object\n", + "pricing_completion object\n", + "pricing_request object\n", + "pricing_image object\n", + "pricing_web_search object\n", + "pricing_internal_reasoning object\n", + "top_provider_context_length float64\n", + "top_provider_max_completion_tokens float64\n", + "top_provider_is_moderated bool\n", + "pricing_input_cache_read object\n", + "pricing_input_cache_write object\n", + "dtype: object" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "import helpers.hpandas as hpandas" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO Processing col_name='name'\n", + "INFO Processing col_name='created'\n", + "INFO Processing col_name='description'\n", + "INFO Processing col_name='context_length'\n", + "INFO Processing col_name='per_request_limits'\n", + "INFO Processing col_name='supported_parameters'\n", + "INFO Processing col_name='architecture_modality'\n", + "INFO Processing col_name='architecture_input_modalities'\n", + "INFO Processing col_name='architecture_output_modalities'\n", + "INFO Processing col_name='architecture_tokenizer'\n", + "INFO Processing col_name='architecture_instruct_type'\n", + "INFO Processing col_name='pricing_prompt'\n", + "INFO Processing col_name='pricing_completion'\n", + "INFO Processing col_name='pricing_request'\n", + "INFO column=pricing_request\n", + "id\n", + "openrouter/auto NaN\n", + "Name: pricing_request, dtype: object\n", + "INFO Processing col_name='pricing_image'\n", + "INFO column=pricing_image\n", + "id\n", + "openrouter/auto NaN\n", + "Name: pricing_image, dtype: object\n", + "INFO Processing col_name='pricing_web_search'\n", + "INFO column=pricing_web_search\n", + "id\n", + "openrouter/auto NaN\n", + "Name: pricing_web_search, dtype: object\n", + "INFO Processing col_name='pricing_internal_reasoning'\n", + "INFO column=pricing_internal_reasoning\n", + "id\n", + "openrouter/auto NaN\n", + "Name: pricing_internal_reasoning, dtype: object\n", + "INFO Processing col_name='top_provider_context_length'\n", + "INFO column=top_provider_context_length\n", + "id\n", + "openrouter/auto NaN\n", + "Name: top_provider_context_length, dtype: float64\n", + "INFO Processing col_name='top_provider_max_completion_tokens'\n", + "INFO column=top_provider_max_completion_tokens\n", + "id\n", + "mistralai/mistral-medium-3 NaN\n", + "arcee-ai/caller-large NaN\n", + "arcee-ai/coder-large NaN\n", + "arcee-ai/arcee-blitz NaN\n", + "microsoft/phi-4-reasoning-plus:free 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"qwen/qwen2.5-coder-7b-instruct NaN\n", + "arliai/qwq-32b-arliai-rpr-v1:free NaN\n", + "agentica-org/deepcoder-14b-preview:free NaN\n", + "moonshotai/kimi-vl-a3b-thinking:free NaN\n", + "x-ai/grok-3-mini-beta NaN\n", + "x-ai/grok-3-beta NaN\n", + "nvidia/llama-3.3-nemotron-super-49b-v1:free NaN\n", + "nvidia/llama-3.3-nemotron-super-49b-v1 NaN\n", + "nvidia/llama-3.1-nemotron-ultra-253b-v1:free NaN\n", + "meta-llama/llama-4-maverick:free NaN\n", + "meta-llama/llama-4-scout:free NaN\n", + "mistral/ministral-8b NaN\n", + "deepseek/deepseek-v3-base:free NaN\n", + "scb10x/llama3.1-typhoon2-8b-instruct NaN\n", + "scb10x/llama3.1-typhoon2-70b-instruct NaN\n", + "allenai/molmo-7b-d:free NaN\n", + "bytedance-research/ui-tars-72b:free NaN\n", + "qwen/qwen2.5-vl-3b-instruct:free NaN\n", + "qwen/qwen2.5-vl-32b-instruct:free NaN\n", + "qwen/qwen2.5-vl-32b-instruct NaN\n", + "deepseek/deepseek-chat-v3-0324:free NaN\n", + "deepseek/deepseek-chat-v3-0324 NaN\n", + 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NaN\n", + "meta-llama/llama-3.1-405b:free NaN\n", + "meta-llama/llama-3.1-405b NaN\n", + "perplexity/llama-3.1-sonar-small-128k-online NaN\n", + "perplexity/llama-3.1-sonar-large-128k-online NaN\n", + "mistralai/codestral-mamba NaN\n", + "google/gemma-2-27b-it NaN\n", + "google/gemma-2-9b-it NaN\n", + "cognitivecomputations/dolphin-mixtral-8x22b NaN\n", + "microsoft/phi-3-mini-128k-instruct NaN\n", + "microsoft/phi-3-medium-128k-instruct NaN\n", + "deepseek/deepseek-coder NaN\n", + "meta-llama/llama-guard-2-8b NaN\n", + "allenai/olmo-7b-instruct NaN\n", + "mistralai/mixtral-8x22b-instruct NaN\n", + "sophosympatheia/midnight-rose-70b NaN\n", + "mistralai/mistral-large NaN\n", + "mistralai/mistral-medium NaN\n", + "mistralai/mistral-small NaN\n", + "mistralai/mistral-tiny NaN\n", + "mistralai/mistral-7b-instruct-v0.2 NaN\n", + "mistralai/mixtral-8x7b-instruct NaN\n", + "openrouter/auto NaN\n", + "jondurbin/airoboros-l2-70b NaN\n", + "mistralai/mistral-7b-instruct-v0.1 NaN\n", + "meta-llama/llama-2-70b-chat NaN\n", + "Name: top_provider_max_completion_tokens, dtype: float64\n", + "INFO Processing col_name='top_provider_is_moderated'\n", + "INFO Processing col_name='pricing_input_cache_read'\n", + "INFO column=pricing_input_cache_read\n", + "id\n", + "mistralai/mistral-medium-3 NaN\n", + "arcee-ai/caller-large NaN\n", + "arcee-ai/spotlight NaN\n", + "arcee-ai/maestro-reasoning NaN\n", + "arcee-ai/virtuoso-large NaN\n", + "arcee-ai/coder-large NaN\n", + "arcee-ai/virtuoso-medium-v2 NaN\n", + "arcee-ai/arcee-blitz NaN\n", + "microsoft/phi-4-reasoning-plus:free NaN\n", + "microsoft/phi-4-reasoning-plus NaN\n", + "microsoft/phi-4-reasoning:free NaN\n", + "qwen/qwen3-0.6b-04-28:free NaN\n", + "inception/mercury-coder-small-beta NaN\n", + "qwen/qwen3-1.7b:free NaN\n", + "qwen/qwen3-4b:free NaN\n", + "opengvlab/internvl3-14b:free NaN\n", + "opengvlab/internvl3-2b:free NaN\n", + "deepseek/deepseek-prover-v2:free NaN\n", + "deepseek/deepseek-prover-v2 NaN\n", + 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"mistralai/mistral-small-3.1-24b-instruct:free NaN\n", + "mistralai/mistral-small-3.1-24b-instruct NaN\n", + "open-r1/olympiccoder-32b:free NaN\n", + "google/gemma-3-1b-it:free NaN\n", + "google/gemma-3-4b-it:free NaN\n", + "google/gemma-3-4b-it NaN\n", + "ai21/jamba-1.6-large NaN\n", + "ai21/jamba-1.6-mini NaN\n", + "google/gemma-3-12b-it:free NaN\n", + "google/gemma-3-12b-it NaN\n", + "cohere/command-a NaN\n", + "openai/gpt-4o-mini-search-preview NaN\n", + "openai/gpt-4o-search-preview NaN\n", + "rekaai/reka-flash-3:free NaN\n", + "google/gemma-3-27b-it:free NaN\n", + "google/gemma-3-27b-it NaN\n", + "thedrummer/anubis-pro-105b-v1 NaN\n", + "thedrummer/skyfall-36b-v2 NaN\n", + "microsoft/phi-4-multimodal-instruct NaN\n", + "perplexity/sonar-reasoning-pro NaN\n", + "perplexity/sonar-pro NaN\n", + "perplexity/sonar-deep-research NaN\n", + "deepseek/deepseek-r1-zero:free NaN\n", + "qwen/qwq-32b:free NaN\n", + "qwen/qwq-32b NaN\n", + "moonshotai/moonlight-16b-a3b-instruct:free NaN\n", + 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"deepseek/deepseek-r1-distill-qwen-14b:free NaN\n", + "deepseek/deepseek-r1-distill-qwen-14b NaN\n", + "perplexity/sonar-reasoning NaN\n", + "perplexity/sonar NaN\n", + "liquid/lfm-7b NaN\n", + "liquid/lfm-3b NaN\n", + "deepseek/deepseek-r1-distill-llama-70b:free NaN\n", + "deepseek/deepseek-r1-distill-llama-70b NaN\n", + "deepseek/deepseek-r1:free NaN\n", + "deepseek/deepseek-r1 NaN\n", + "minimax/minimax-01 NaN\n", + "mistralai/codestral-2501 NaN\n", + "microsoft/phi-4 NaN\n", + "deepseek/deepseek-chat:free NaN\n", + "deepseek/deepseek-chat NaN\n", + "sao10k/l3.3-euryale-70b NaN\n", + "eva-unit-01/eva-llama-3.33-70b NaN\n", + "x-ai/grok-2-vision-1212 NaN\n", + "x-ai/grok-2-1212 NaN\n", + "cohere/command-r7b-12-2024 NaN\n", + "google/gemini-2.0-flash-exp:free NaN\n", + "meta-llama/llama-3.3-70b-instruct:free NaN\n", + "meta-llama/llama-3.3-70b-instruct NaN\n", + "amazon/nova-lite-v1 NaN\n", + "amazon/nova-micro-v1 NaN\n", + "amazon/nova-pro-v1 NaN\n", + "qwen/qwq-32b-preview:free NaN\n", + "qwen/qwq-32b-preview NaN\n", + "google/learnlm-1.5-pro-experimental:free NaN\n", + "eva-unit-01/eva-qwen-2.5-72b NaN\n", + "mistralai/mistral-large-2411 NaN\n", + "mistralai/mistral-large-2407 NaN\n", + "mistralai/pixtral-large-2411 NaN\n", + "x-ai/grok-vision-beta NaN\n", + "infermatic/mn-inferor-12b NaN\n", + "qwen/qwen-2.5-coder-32b-instruct:free NaN\n", + "qwen/qwen-2.5-coder-32b-instruct NaN\n", + "raifle/sorcererlm-8x22b NaN\n", + "eva-unit-01/eva-qwen-2.5-32b NaN\n", + "thedrummer/unslopnemo-12b NaN\n", + "neversleep/llama-3.1-lumimaid-70b NaN\n", + "anthracite-org/magnum-v4-72b NaN\n", + "x-ai/grok-beta NaN\n", + "mistralai/ministral-8b NaN\n", + "mistralai/ministral-3b NaN\n", + "qwen/qwen-2.5-7b-instruct:free NaN\n", + "qwen/qwen-2.5-7b-instruct NaN\n", + "nvidia/llama-3.1-nemotron-70b-instruct NaN\n", + "inflection/inflection-3-productivity NaN\n", + "inflection/inflection-3-pi NaN\n", + "thedrummer/rocinante-12b NaN\n", + "anthracite-org/magnum-v2-72b NaN\n", + 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"sao10k/l3-lunaris-8b NaN\n", + "aetherwiing/mn-starcannon-12b NaN\n", + "meta-llama/llama-3.1-405b:free NaN\n", + "meta-llama/llama-3.1-405b NaN\n", + "nothingiisreal/mn-celeste-12b NaN\n", + "perplexity/llama-3.1-sonar-small-128k-online NaN\n", + "perplexity/llama-3.1-sonar-large-128k-online NaN\n", + "meta-llama/llama-3.1-8b-instruct:free NaN\n", + "meta-llama/llama-3.1-8b-instruct NaN\n", + "meta-llama/llama-3.1-405b-instruct NaN\n", + "meta-llama/llama-3.1-70b-instruct NaN\n", + "mistralai/codestral-mamba NaN\n", + "mistralai/mistral-nemo:free NaN\n", + "mistralai/mistral-nemo NaN\n", + "google/gemma-2-27b-it NaN\n", + "alpindale/magnum-72b NaN\n", + "google/gemma-2-9b-it:free NaN\n", + "google/gemma-2-9b-it NaN\n", + "01-ai/yi-large NaN\n", + "ai21/jamba-instruct NaN\n", + "sao10k/l3-euryale-70b NaN\n", + "cognitivecomputations/dolphin-mixtral-8x22b NaN\n", + "qwen/qwen-2-72b-instruct NaN\n", + "mistralai/mistral-7b-instruct:free NaN\n", + "mistralai/mistral-7b-instruct NaN\n", + 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This merge...\n", + "296 Claude 2 delivers advancements in key capabili...\n", + "297 Claude 2 delivers advancements in key capabili...\n", + "298 Claude 2 delivers advancements in key capabili...\n", + "299 Claude 2 delivers advancements in key capabili...\n", + "300 A wild 7B parameter model that merges several ...\n", + "301 A large LLM created by combining two fine-tune...\n", + "302 Your prompt will be processed by a meta-model ...\n", + "303 An older GPT-3.5 Turbo model with improved ins...\n", + "304 The latest GPT-4 Turbo model with vision capab...\n", + "305 A Llama 2 70B fine-tune using synthetic data (...\n", + "306 This model is a variant of GPT-3.5 Turbo tuned...\n", + "307 A 7.3B parameter model that outperforms Llama ...\n", + "308 A blend of the new Pygmalion-13b and MythoMax....\n", + "309 This model offers four times the context lengt...\n", + "310 GPT-4-32k is an extended version of GPT-4, wit...\n", + "311 GPT-4-32k is an extended version of GPT-4, wit...\n", + "312 An attempt to recreate Claude-style verbosity,...\n", + "313 Anthropic's flagship model. 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"49 GPT\n", + "50 Other\n", + "51 Other\n", + "52 Other\n", + "53 Other\n", + "54 Other\n", + "55 Grok\n", + "56 Grok\n", + "57 Other\n", + "58 Other\n", + "59 Llama3\n", + "60 Other\n", + "61 Other\n", + "62 Other\n", + "63 Other\n", + "64 Other\n", + "65 Mistral\n", + "66 DeepSeek\n", + "67 Llama3\n", + "68 Llama3\n", + "69 Other\n", + "70 Other\n", + "71 Qwen\n", + "72 Gemini\n", + "73 Qwen\n", + "74 Qwen\n", + "75 DeepSeek\n", + "76 DeepSeek\n", + "77 Other\n", + "78 GPT\n", + "79 Mistral\n", + "80 Mistral\n", + "81 Other\n", + "82 Gemini\n", + "83 Gemini\n", + "84 Gemini\n", + "85 Other\n", + "86 Other\n", + "87 Gemini\n", + "88 Gemini\n", + "89 Other\n", + "90 GPT\n", + "91 GPT\n", + "92 Other\n", + "93 Gemini\n", + "94 Gemini\n", + "95 Other\n", + "96 Other\n", + "97 Other\n", + "98 Other\n", + "99 Other\n", + "100 Other\n", + "101 Other\n", + "102 Qwen\n", + "103 Qwen\n", + "104 Other\n", + "105 Other\n", + "106 GPT\n", + "107 Gemini\n", + "108 Claude\n", + "109 Claude\n", + "110 Claude\n", + "111 DeepSeek\n", + "112 Mistral\n", + "113 Other\n", + "114 Other\n", + "115 Llama3\n", + "116 Other\n", + "117 Llama3\n", + "118 Gemini\n", + "119 Qwen\n", + "120 Other\n", + "121 Other\n", + "122 Other\n", + "123 Qwen\n", + "124 Qwen\n", + "125 Qwen\n", + "126 Qwen\n", + "127 Qwen\n", + "128 Qwen\n", + "129 Other\n", + "130 Other\n", + "131 Mistral\n", + "132 Mistral\n", + "133 Qwen\n", + "134 Qwen\n", + "135 Qwen\n", + "136 Qwen\n", + "137 Other\n", + "138 Other\n", + "139 Other\n", + "140 Other\n", + "141 Llama3\n", + "142 Llama3\n", + "143 DeepSeek\n", + "144 DeepSeek\n", + "145 Other\n", + "146 Mistral\n", + "147 Other\n", + "148 DeepSeek\n", + "149 DeepSeek\n", + "150 Llama3\n", + "151 GPT\n", + "152 Llama3\n", + "153 Grok\n", + "154 Grok\n", + "155 Cohere\n", + "156 Gemini\n", + "157 Llama3\n", + "158 Llama3\n", + "159 Nova\n", + "160 Nova\n", + "161 Nova\n", + "162 Qwen\n", + "163 Qwen\n", + "164 Gemini\n", + "165 Qwen\n", + "166 GPT\n", + "167 Mistral\n", + "168 Mistral\n", + "169 Mistral\n", + "170 Grok\n", + "171 Mistral\n", + "172 Qwen\n", + "173 Qwen\n", + "174 Mistral\n", + "175 Qwen\n", + "176 Mistral\n", + "177 Claude\n", + "178 Claude\n", + "179 Claude\n", + "180 Claude\n", + "181 Llama3\n", + "182 Qwen\n", + "183 Claude\n", + "184 Claude\n", + "185 Grok\n", + "186 Mistral\n", + "187 Mistral\n", + "188 Qwen\n", + "189 Qwen\n", + "190 Llama3\n", + "191 Other\n", + "192 Other\n", + "193 Gemini\n", + "194 Qwen\n", + "195 Qwen\n", + "196 Other\n", + "197 Llama3\n", + "198 Llama3\n", + "199 Llama3\n", + "200 Llama3\n", + "201 Llama3\n", + "202 Llama3\n", + "203 Llama3\n", + "204 Qwen\n", + "205 Qwen\n", + "206 Qwen\n", + "207 Llama3\n", + "208 GPT\n", + "209 GPT\n", + "210 GPT\n", + "211 GPT\n", + "212 Mistral\n", + "213 Cohere\n", + "214 Cohere\n", + "215 Qwen\n", + "216 Qwen\n", + "217 Llama3\n", + "218 Gemini\n", + "219 Other\n", + "220 Llama3\n", + "221 Llama3\n", + "222 GPT\n", + "223 Llama3\n", + "224 Mistral\n", + "225 GPT\n", + "226 Llama3\n", + "227 Llama3\n", + "228 Mistral\n", + "229 Llama3\n", + "230 Llama3\n", + "231 Llama3\n", + "232 Llama3\n", + "233 Llama3\n", + "234 Llama3\n", + "235 Mistral\n", + "236 Mistral\n", + "237 Mistral\n", + "238 GPT\n", + "239 GPT\n", + "240 Gemini\n", + "241 Qwen\n", + "242 Gemini\n", + "243 Gemini\n", + "244 Yi\n", + "245 Other\n", + "246 Claude\n", + "247 Claude\n", + "248 Llama3\n", + "249 Mistral\n", + "250 Qwen\n", + "251 Mistral\n", + "252 Mistral\n", + "253 Llama3\n", + "254 Mistral\n", + "255 Other\n", + "256 Other\n", + "257 Llama3\n", + "258 Other\n", + "259 Gemini\n", + "260 GPT\n", + "261 GPT\n", + "262 Llama3\n", + "263 GPT\n", + "264 Other\n", + "265 Llama3\n", + "266 Llama3\n", + "267 Llama2\n", + "268 Llama3\n", + "269 Llama3\n", + "270 Mistral\n", + "271 Mistral\n", + "272 Gemini\n", + "273 GPT\n", + "274 Cohere\n", + "275 Cohere\n", + "276 Llama2\n", + "277 Cohere\n", + "278 Cohere\n", + "279 Claude\n", + "280 Claude\n", + "281 Claude\n", + "282 Claude\n", + "283 Claude\n", + "284 Claude\n", + "285 Cohere\n", + "286 Mistral\n", + "287 GPT\n", + "288 GPT\n", + "289 Mistral\n", + "290 Mistral\n", + "291 Mistral\n", + "292 Mistral\n", + "293 Mistral\n", + "294 Mistral\n", + "295 Llama2\n", + "296 Claude\n", + "297 Claude\n", + "298 Claude\n", + "299 Claude\n", + "300 Mistral\n", + "301 Llama2\n", + "302 Router\n", + "303 GPT\n", + "304 GPT\n", + "305 Llama2\n", + "306 GPT\n", + "307 Mistral\n", + "308 Llama2\n", + "309 GPT\n", + "310 GPT\n", + "311 GPT\n", + "312 Llama2\n", + "313 Claude\n", + "314 Claude\n", + "315 Llama2\n", + "316 Llama2\n", + "317 Llama2\n", + "318 GPT\n", + "319 GPT\n", + "320 GPT\n", + "321 GPT\n", + "Name: architecture_tokenizer, dtype: object)\n", + "({'is_bool': 0.0, 'is_int': 0.0, 'is_float': 0.0, 'is_string': 1.0}, 0 None\n", + "1 None\n", + "2 None\n", + "3 None\n", + "4 None\n", + "5 None\n", + "6 None\n", + "7 None\n", + "8 None\n", + "9 None\n", + "10 None\n", + "11 None\n", + "12 None\n", + "13 None\n", + "14 None\n", + "15 None\n", + "16 None\n", + "17 None\n", + "18 None\n", + "19 None\n", + "20 None\n", + "21 None\n", + "22 None\n", + "23 None\n", + "24 None\n", + "25 None\n", + "26 None\n", + "27 None\n", + "28 None\n", + "29 None\n", + "30 None\n", + "31 deepseek-r1\n", + "32 deepseek-r1\n", + "33 deepseek-r1\n", + "34 None\n", + "35 deepseek-r1\n", + "36 deepseek-r1\n", + "37 deepseek-r1\n", + "38 None\n", + "39 None\n", + "40 None\n", + "41 None\n", + "42 None\n", + "43 None\n", + "44 None\n", + "45 None\n", + "46 None\n", + "47 None\n", + "48 None\n", + "49 None\n", + "50 code-llama\n", + "51 alpaca\n", + "52 deepseek-r1\n", + "53 deepseek-r1\n", + "54 None\n", + "55 None\n", + "56 None\n", + "57 None\n", + "58 None\n", + "59 None\n", + "60 None\n", + "61 None\n", + "62 None\n", + "63 None\n", + "64 None\n", + "65 None\n", + "66 None\n", + "67 llama3\n", + "68 llama3\n", + "69 None\n", + "70 None\n", + "71 None\n", + "72 None\n", + "73 None\n", + "74 None\n", + "75 None\n", + "76 None\n", + "77 None\n", + "78 None\n", + "79 None\n", + "80 None\n", + "81 deepseek-r1\n", + "82 gemma\n", + "83 gemma\n", + "84 gemma\n", + "85 None\n", + "86 None\n", + "87 gemma\n", + "88 gemma\n", + "89 None\n", + "90 None\n", + "91 None\n", + "92 None\n", + "93 gemma\n", + "94 gemma\n", + "95 None\n", + "96 None\n", + "97 None\n", + "98 deepseek-r1\n", + "99 None\n", + "100 deepseek-r1\n", + "101 deepseek-r1\n", + "102 qwq\n", + "103 qwq\n", + "104 None\n", + "105 None\n", + "106 None\n", + "107 None\n", + "108 None\n", + "109 None\n", + "110 None\n", + "111 deepseek-r1\n", + "112 None\n", + "113 deepseek-r1\n", + "114 None\n", + "115 none\n", + "116 None\n", + "117 deepseek-r1\n", + "118 None\n", + "119 None\n", + "120 None\n", + "121 None\n", + "122 None\n", + "123 None\n", + "124 None\n", + "125 None\n", + "126 None\n", + "127 None\n", + "128 None\n", + "129 None\n", + "130 deepseek-r1\n", + "131 None\n", + "132 None\n", + "133 deepseek-r1\n", + "134 deepseek-r1\n", + "135 deepseek-r1\n", + "136 deepseek-r1\n", + "137 deepseek-r1\n", + "138 None\n", + "139 chatml\n", + "140 chatml\n", + "141 deepseek-r1\n", + "142 deepseek-r1\n", + "143 deepseek-r1\n", + "144 deepseek-r1\n", + "145 None\n", + "146 None\n", + "147 None\n", + "148 None\n", + "149 None\n", + "150 llama3\n", + "151 None\n", + "152 llama3\n", + "153 None\n", + "154 None\n", + "155 None\n", + "156 None\n", + "157 llama3\n", + "158 llama3\n", + "159 None\n", + "160 None\n", + "161 None\n", + "162 deepseek-r1\n", + "163 deepseek-r1\n", + "164 None\n", + "165 chatml\n", + "166 None\n", + "167 None\n", + "168 None\n", + "169 None\n", + "170 None\n", + "171 mistral\n", + "172 chatml\n", + "173 chatml\n", + "174 vicuna\n", + "175 chatml\n", + "176 mistral\n", + "177 None\n", + "178 None\n", + "179 None\n", + "180 None\n", + "181 llama3\n", + "182 chatml\n", + "183 None\n", + "184 None\n", + "185 None\n", + "186 None\n", + "187 None\n", + "188 chatml\n", + "189 chatml\n", + "190 llama3\n", + "191 None\n", + "192 None\n", + "193 None\n", + "194 chatml\n", + "195 chatml\n", + "196 chatml\n", + "197 llama3\n", + "198 llama3\n", + "199 llama3\n", + "200 llama3\n", + "201 llama3\n", + "202 llama3\n", + "203 llama3\n", + "204 chatml\n", + "205 chatml\n", + "206 None\n", + "207 llama3\n", + "208 None\n", + "209 None\n", + "210 None\n", + "211 None\n", + "212 None\n", + "213 None\n", + "214 None\n", + "215 None\n", + "216 None\n", + "217 llama3\n", + "218 None\n", + "219 phi3\n", + "220 chatml\n", + "221 chatml\n", + "222 None\n", + "223 llama3\n", + "224 chatml\n", + "225 None\n", + "226 none\n", + "227 none\n", + "228 chatml\n", + "229 None\n", + "230 None\n", + "231 llama3\n", + "232 llama3\n", + "233 llama3\n", + "234 llama3\n", + "235 None\n", + "236 mistral\n", + "237 mistral\n", + "238 None\n", + "239 None\n", + "240 gemma\n", + "241 chatml\n", + "242 gemma\n", + "243 gemma\n", + "244 None\n", + "245 None\n", + "246 None\n", + "247 None\n", + "248 llama3\n", + "249 chatml\n", + "250 chatml\n", + "251 mistral\n", + "252 mistral\n", + "253 chatml\n", + "254 mistral\n", + "255 phi3\n", + "256 phi3\n", + "257 llama3\n", + "258 None\n", + "259 None\n", + "260 None\n", + "261 None\n", + "262 none\n", + "263 None\n", + "264 zephyr\n", + "265 llama3\n", + "266 llama3\n", + "267 alpaca\n", + "268 llama3\n", + "269 llama3\n", + "270 mistral\n", + "271 vicuna\n", + "272 None\n", + "273 None\n", + "274 None\n", + "275 None\n", + "276 airoboros\n", + "277 None\n", + "278 None\n", + "279 None\n", + "280 None\n", + "281 None\n", + "282 None\n", + "283 None\n", + "284 None\n", + "285 None\n", + "286 None\n", + "287 None\n", + "288 None\n", + "289 chatml\n", + "290 None\n", + "291 None\n", + "292 None\n", + "293 mistral\n", + "294 mistral\n", + "295 alpaca\n", + "296 None\n", + "297 None\n", + "298 None\n", + "299 None\n", + "300 alpaca\n", + "301 airoboros\n", + "302 None\n", + "303 None\n", + "304 None\n", + "305 airoboros\n", + "306 chatml\n", + "307 mistral\n", + "308 alpaca\n", + "309 None\n", + "310 None\n", + "311 None\n", + "312 alpaca\n", + "313 None\n", + "314 None\n", + "315 alpaca\n", + "316 alpaca\n", + "317 llama2\n", + "318 None\n", + "319 None\n", + "320 None\n", + "321 None\n", + "Name: architecture_instruct_type, dtype: object)\n", + "({'is_bool': 0.2236024844720497, 'is_int': 0.2267080745341615, 'is_float': 1.0, 'is_string': 1.0}, 0 4.000000e-07\n", + "1 1.250000e-06\n", + "2 5.500000e-07\n", + "3 1.800000e-07\n", + "4 9.000000e-07\n", + "5 7.500000e-07\n", + "6 5.000000e-07\n", + "7 5.000000e-07\n", + "8 4.500000e-07\n", + "9 0.000000e+00\n", + "10 7.000000e-08\n", + "11 0.000000e+00\n", + "12 0.000000e+00\n", + "13 2.500000e-07\n", + "14 0.000000e+00\n", + "15 0.000000e+00\n", + "16 0.000000e+00\n", + "17 0.000000e+00\n", + "18 0.000000e+00\n", + "19 5.000000e-07\n", + "20 5.000000e-08\n", + "21 0.000000e+00\n", + "22 1.000000e-07\n", + "23 0.000000e+00\n", + "24 3.500000e-08\n", + "25 0.000000e+00\n", + "26 7.000000e-08\n", + "27 0.000000e+00\n", + "28 1.000000e-07\n", + "29 0.000000e+00\n", + "30 1.400000e-07\n", + "31 0.000000e+00\n", + "32 2.400000e-07\n", + "33 0.000000e+00\n", + "34 0.000000e+00\n", + "35 0.000000e+00\n", + "36 0.000000e+00\n", + "37 2.400000e-07\n", + "38 0.000000e+00\n", + "39 2.400000e-07\n", + "40 1.500000e-07\n", + "41 1.500000e-07\n", + "42 1.100000e-06\n", + "43 1.000000e-05\n", + "44 1.100000e-06\n", + "45 0.000000e+00\n", + "46 1.000000e-08\n", + "47 2.000000e-06\n", + "48 4.000000e-07\n", + "49 1.000000e-07\n", + "50 8.000000e-07\n", + "51 8.000000e-07\n", + "52 0.000000e+00\n", + "53 0.000000e+00\n", + "54 0.000000e+00\n", + "55 3.000000e-07\n", + "56 3.000000e-06\n", + "57 0.000000e+00\n", + "58 1.300000e-07\n", + "59 0.000000e+00\n", + "60 0.000000e+00\n", + "61 1.700000e-07\n", + "62 0.000000e+00\n", + "63 8.000000e-08\n", + "64 2.600000e-06\n", + "65 1.000000e-07\n", + "66 0.000000e+00\n", + "67 1.800000e-07\n", + "68 8.800000e-07\n", + "69 0.000000e+00\n", + "70 0.000000e+00\n", + "71 0.000000e+00\n", + "72 0.000000e+00\n", + "73 0.000000e+00\n", + "74 9.000000e-07\n", + "75 0.000000e+00\n", + "76 3.000000e-07\n", + "77 0.000000e+00\n", + "78 1.500000e-04\n", + "79 0.000000e+00\n", + "80 5.000000e-08\n", + "81 0.000000e+00\n", + "82 0.000000e+00\n", + "83 0.000000e+00\n", + "84 2.000000e-08\n", + "85 2.000000e-06\n", + "86 2.000000e-07\n", + "87 0.000000e+00\n", + "88 5.000000e-08\n", + "89 2.500000e-06\n", + "90 1.500000e-07\n", + "91 2.500000e-06\n", + "92 0.000000e+00\n", + "93 0.000000e+00\n", + "94 1.000000e-07\n", + "95 8.000000e-07\n", + "96 5.000000e-07\n", + "97 5.000000e-08\n", + "98 2.000000e-06\n", + "99 3.000000e-06\n", + "100 2.000000e-06\n", + "101 0.000000e+00\n", + "102 0.000000e+00\n", + "103 1.500000e-07\n", + "104 0.000000e+00\n", + "105 0.000000e+00\n", + "106 7.500000e-05\n", + "107 7.500000e-08\n", + "108 3.000000e-06\n", + "109 3.000000e-06\n", + "110 3.000000e-06\n", + "111 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errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m#b.apply(lambda x: float(x).is_integer())\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6637\u001b[0m results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 6638\u001b[0m ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 6639\u001b[0m ]\n\u001b[1;32m 6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6642\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6643\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6644\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m 6645\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/internals/managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 428\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 431\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 433\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 434\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/internals/managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 361\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 363\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 364\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 756\u001b[0m values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :] \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[0;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m 762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/dtypes/astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 234\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 237\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m 239\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m 240\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/dtypes/astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 179\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 182\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/dtypes/astype.py:101\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mensure_string_array(\n\u001b[1;32m 97\u001b[0m arr, skipna\u001b[38;5;241m=\u001b[39mskipna, convert_na_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 98\u001b[0m )\u001b[38;5;241m.\u001b[39mreshape(shape)\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(arr\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39mfloating) \u001b[38;5;129;01mand\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_astype_float_to_int_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m 104\u001b[0m \u001b[38;5;66;03m# if we have a datetime/timedelta array of objects\u001b[39;00m\n\u001b[1;32m 105\u001b[0m \u001b[38;5;66;03m# then coerce to datetime64[ns] and use DatetimeArray.astype\u001b[39;00m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(dtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/dtypes/astype.py:145\u001b[0m, in \u001b[0;36m_astype_float_to_int_nansafe\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03mastype with a check preventing converting NaN to an meaningless integer value.\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(values)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m IntCastingNaNError(\n\u001b[1;32m 146\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert non-finite values (NA or inf) to integer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 147\u001b[0m )\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 149\u001b[0m \u001b[38;5;66;03m# GH#45151\u001b[39;00m\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (values \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mall():\n", + "\u001b[0;31mIntCastingNaNError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer" + ] + } + ], + "source": [ + "a = pd.Series([0, \"hello\", None])\n", + "b = pd.to_numeric(a, errors=\"coerce\")\n", + "#b.apply(lambda x: float(x).is_integer())\n", + "b.astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 True\n", + "7 True\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 True\n", + "14 True\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 True\n", + "19 True\n", + "20 True\n", + "21 True\n", + "22 True\n", + "23 True\n", + "24 True\n", + "25 True\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 True\n", + "30 True\n", + "31 True\n", + "32 True\n", + "33 True\n", + "34 True\n", + "35 True\n", + "36 True\n", + "37 True\n", + "38 True\n", + "39 True\n", + "40 True\n", + "41 True\n", + "42 True\n", + "43 True\n", + "44 True\n", + "45 True\n", + "46 True\n", + "47 True\n", + "48 True\n", + "49 True\n", + "50 True\n", + "51 True\n", + "52 True\n", + "53 True\n", + "54 True\n", + "55 True\n", + "56 True\n", + "57 True\n", + "58 True\n", + "59 True\n", + "60 True\n", + "61 True\n", + "62 True\n", + "63 True\n", + "64 True\n", + "65 True\n", + "66 True\n", + "67 True\n", + "68 True\n", + "69 True\n", + "70 True\n", + "71 True\n", + "72 True\n", + "73 True\n", + "74 True\n", + "75 True\n", + "76 True\n", + "77 True\n", + "78 True\n", + "79 True\n", + "80 True\n", + "81 True\n", + "82 True\n", + "83 True\n", + "84 True\n", + "85 True\n", + "86 True\n", + "87 True\n", + "88 True\n", + "89 True\n", + "90 True\n", + "91 True\n", + "92 True\n", + "93 True\n", + "94 True\n", + "95 True\n", + "96 True\n", + "97 True\n", + "98 True\n", + "99 True\n", + "100 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"179 anthropic/claude-3.5-haiku-20241022:beta 200000 0.0000008 0.000004\n", + "95 thedrummer/anubis-pro-105b-v1 131072 0.0000008 0.000001\n", + "180 anthropic/claude-3.5-haiku-20241022 200000 0.0000008 0.000004\n", + "51 alfredpros/codellama-7b-instruct-solidity 4096 0.0000008 0.0000012\n", + "50 eleutherai/llemma_7b 4096 0.0000008 0.0000012\n", + "267 sao10k/fimbulvetr-11b-v2 4096 0.0000008 0.0000012\n", + "276 sophosympatheia/midnight-rose-70b 4096 0.0000008 0.0000008\n", + "123 qwen/qwen-vl-max 7500 0.0000008 0.0000032\n", + "161 amazon/nova-pro-v1 300000 0.0000008 0.0000032\n", + "171 infermatic/mn-inferor-12b 16384 0.0000008 0.0000012\n", + "300 undi95/toppy-m-7b 4096 0.0000008 0.0000012\n", + "177 anthropic/claude-3.5-haiku:beta 200000 0.0000008 0.000004\n", + "178 anthropic/claude-3.5-haiku 200000 0.0000008 0.000004\n", + "228 nothingiisreal/mn-celeste-12b 16384 0.0000008 0.0000012\n", + "68 scb10x/llama3.1-typhoon2-70b-instruct 8192 0.00000088 0.00000088\n", + "74 qwen/qwen2.5-vl-32b-instruct 128000 0.0000009 0.0000009\n", + "249 cognitivecomputations/dolphin-mixtral-8x22b 16000 0.0000009 0.0000009\n", + "4 arcee-ai/maestro-reasoning 131072 0.0000009 0.0000033\n", + "317 meta-llama/llama-2-70b-chat 4096 0.0000009 0.0000009\n", + "250 qwen/qwen-2-72b-instruct 32768 0.0000009 0.0000009\n", + "230 perplexity/llama-3.1-sonar-large-128k-online 127072 0.000001 0.000001\n", + "287 openai/gpt-3.5-turbo-0613 4095 0.000001 0.000002\n", + "277 cohere/command 4096 0.000001 0.000002\n", + "138 perplexity/sonar 127072 0.000001 0.000001\n", + "137 perplexity/sonar-reasoning 127000 0.000001 0.000005\n", + "303 openai/gpt-3.5-turbo-1106 16385 0.000001 0.000002\n", + "42 openai/o4-mini-high 200000 0.0000011 0.0000044\n", + "210 openai/o1-mini 128000 0.0000011 0.0000044\n", + "211 openai/o1-mini-2024-09-12 128000 0.0000011 0.0000044\n", + "44 openai/o4-mini 200000 0.0000011 0.0000044\n", + "129 openai/o3-mini 200000 0.0000011 0.0000044\n", + "116 openai/o3-mini-high 200000 0.0000011 0.0000044\n", + "312 mancer/weaver 8000 0.000001125 0.000001125\n", + "201 meta-llama/llama-3.2-90b-vision-instruct 131072 0.0000012 0.0000012\n", + "272 google/gemini-pro-1.5 2000000 0.00000125 0.000005\n", + "1 google/gemini-2.5-pro-preview 1048576 0.00000125 0.00001\n", + "248 sao10k/l3-euryale-70b 8192 0.00000148 0.00000148\n", + "181 neversleep/llama-3.1-lumimaid-70b 16384 0.0000015 0.00000225\n", + "306 openai/gpt-3.5-turbo-instruct 4095 0.0000015 0.000002\n", + "182 anthracite-org/magnum-v4-72b 16384 0.0000015 0.00000225\n", + "128 qwen/qwen-max 32768 0.0000016 0.0000064\n", + "169 mistralai/pixtral-large-2411 131072 0.000002 0.000006\n", + "286 mistralai/mistral-large 128000 0.000002 0.000006\n", + "85 ai21/jamba-1.6-large 256000 0.000002 0.000008\n", + "154 x-ai/grok-2-1212 131072 0.000002 0.00001\n", + "47 openai/gpt-4.1 1047576 0.000002 0.000008\n", + "100 perplexity/sonar-deep-research 128000 0.000002 0.000008\n", + "227 meta-llama/llama-3.1-405b 32768 0.000002 0.000002\n", + "153 x-ai/grok-2-vision-1212 32768 0.000002 0.00001\n", + "168 mistralai/mistral-large-2407 131072 0.000002 0.000006\n", + "98 perplexity/sonar-reasoning-pro 128000 0.000002 0.000008\n", + "111 perplexity/r1-1776 128000 0.000002 0.000008\n", + "167 mistralai/mistral-large-2411 131072 0.000002 0.000006\n", + "166 openai/gpt-4o-2024-11-20 128000 0.0000025 0.00001\n", + "225 openai/gpt-4o-2024-08-06 128000 0.0000025 0.00001\n", + "260 openai/gpt-4o 128000 0.0000025 0.00001\n", + "192 inflection/inflection-3-pi 8000 0.0000025 0.00001\n", + "91 openai/gpt-4o-search-preview 128000 0.0000025 0.00001\n", + "213 cohere/command-r-plus-08-2024 128000 0.0000025 0.00001\n", + "191 inflection/inflection-3-productivity 8000 0.0000025 0.00001\n", + "89 cohere/command-a 256000 0.0000025 0.00001\n", + "64 all-hands/openhands-lm-32b-v0.1 16384 0.0000026 0.0000034\n", + "175 eva-unit-01/eva-qwen-2.5-32b 16384 0.0000026 0.0000034\n", + "290 mistralai/mistral-medium 32768 0.00000275 0.0000081\n", + "195 anthracite-org/magnum-v2-72b 32768 0.000003 0.000003\n", + "284 anthropic/claude-3-sonnet 200000 0.000003 0.000015\n", + "283 anthropic/claude-3-sonnet:beta 200000 0.000003 0.000015\n", + "309 openai/gpt-3.5-turbo-16k 16385 0.000003 0.000004\n", + "184 anthropic/claude-3.5-sonnet 200000 0.000003 0.000015\n", + "183 anthropic/claude-3.5-sonnet:beta 200000 0.000003 0.000015\n", + "275 cohere/command-r-plus-04-2024 128000 0.000003 0.000015\n", + "274 cohere/command-r-plus 128000 0.000003 0.000015\n", + "109 anthropic/claude-3.7-sonnet:thinking 200000 0.000003 0.000015\n", + "110 anthropic/claude-3.7-sonnet:beta 200000 0.000003 0.000015\n", + "99 perplexity/sonar-pro 200000 0.000003 0.000015\n", + "244 01-ai/yi-large 32768 0.000003 0.000003\n", + "246 anthropic/claude-3.5-sonnet-20240620:beta 200000 0.000003 0.000015\n", + "247 anthropic/claude-3.5-sonnet-20240620 200000 0.000003 0.000015\n", + "56 x-ai/grok-3-beta 131072 0.000003 0.000015\n", + "108 anthropic/claude-3.7-sonnet 200000 0.000003 0.000015\n", + "152 eva-unit-01/eva-llama-3.33-70b 16384 0.000004 0.000006\n", + "257 neversleep/llama-3-lumimaid-70b 8192 0.000004 0.000006\n", + "241 alpindale/magnum-72b 16384 0.000004 0.000006\n", + "165 eva-unit-01/eva-qwen-2.5-72b 16384 0.000004 0.000006\n", + "120 aion-labs/aion-1.0 131072 0.000004 0.000008\n", + "174 raifle/sorcererlm-8x22b 16000 0.0000045 0.0000045\n", + "263 openai/gpt-4o-2024-05-13 128000 0.000005 0.000015\n", + "222 openai/chatgpt-4o-latest 128000 0.000005 0.000015\n", + "170 x-ai/grok-vision-beta 8192 0.000005 0.000015\n", + "185 x-ai/grok-beta 131072 0.000005 0.000015\n", + "261 openai/gpt-4o:extended 128000 0.000006 0.000018\n", + "301 alpindale/goliath-120b 6144 0.0000065625 0.000009375\n", + "313 anthropic/claude-2.0:beta 100000 0.000008 0.000024\n", + "297 anthropic/claude-2.1 200000 0.000008 0.000024\n", + "299 anthropic/claude-2 200000 0.000008 0.000024\n", + "298 anthropic/claude-2:beta 200000 0.000008 0.000024\n", + "314 anthropic/claude-2.0 100000 0.000008 0.000024\n", + "296 anthropic/claude-2.1:beta 200000 0.000008 0.000024\n", + "304 openai/gpt-4-1106-preview 128000 0.00001 0.00003\n", + "43 openai/o3 200000 0.00001 0.00004\n", + "273 openai/gpt-4-turbo 128000 0.00001 0.00003\n", + "288 openai/gpt-4-turbo-preview 128000 0.00001 0.00003\n", + "151 openai/o1 200000 0.000015 0.00006\n", + "282 anthropic/claude-3-opus 200000 0.000015 0.000075\n", + "281 anthropic/claude-3-opus:beta 200000 0.000015 0.000075\n", + "208 openai/o1-preview 128000 0.000015 0.00006\n", + "209 openai/o1-preview-2024-09-12 128000 0.000015 0.00006\n", + "321 openai/gpt-4-0314 8191 0.00003 0.00006\n", + "320 openai/gpt-4 8191 0.00003 0.00006\n", + "311 openai/gpt-4-32k-0314 32767 0.00006 0.00012\n", + "310 openai/gpt-4-32k 32767 0.00006 0.00012\n", + "106 openai/gpt-4.5-preview 128000 0.000075 0.00015\n", + "78 openai/o1-pro 200000 0.00015 0.0006" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(\"pricing_prompt\")[col_names]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[[\"pricing_prompt\", \"pricing_completion\"]].plot.scatter(x=\"pricing_prompt\", y=\"pricing_completion\")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for /: 'str' and 'str'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py:218\u001b[0m, in \u001b[0;36m_na_arithmetic_op\u001b[0;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 218\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py:242\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m(op, a, b, use_numexpr)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_numexpr:\n\u001b[1;32m 241\u001b[0m \u001b[38;5;66;03m# error: \"None\" not callable\u001b[39;00m\n\u001b[0;32m--> 242\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_evaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_str\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _evaluate_standard(op, op_str, a, b)\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py:73\u001b[0m, in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m 72\u001b[0m _store_test_result(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'str'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[46], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice_ratio\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpricing_completion\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpricing_prompt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/ops/common.py:76\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer..new_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m 74\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[0;32m---> 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/arraylike.py:210\u001b[0m, in \u001b[0;36mOpsMixin.__truediv__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__truediv__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__truediv__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m--> 210\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtruediv\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/series.py:6135\u001b[0m, in \u001b[0;36mSeries._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m 6133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_arith_method\u001b[39m(\u001b[38;5;28mself\u001b[39m, other, op):\n\u001b[1;32m 6134\u001b[0m \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_align_for_op(other)\n\u001b[0;32m-> 6135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mIndexOpsMixin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/base.py:1382\u001b[0m, in \u001b[0;36mIndexOpsMixin._arith_method\u001b[0;34m(self, other, op)\u001b[0m\n\u001b[1;32m 1379\u001b[0m rvalues \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(rvalues\u001b[38;5;241m.\u001b[39mstart, rvalues\u001b[38;5;241m.\u001b[39mstop, rvalues\u001b[38;5;241m.\u001b[39mstep)\n\u001b[1;32m 1381\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1382\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marithmetic_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(result, name\u001b[38;5;241m=\u001b[39mres_name)\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py:283\u001b[0m, in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m 279\u001b[0m _bool_arith_check(op, left, right) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;66;03m# error: Argument 1 to \"_na_arithmetic_op\" has incompatible type\u001b[39;00m\n\u001b[1;32m 282\u001b[0m \u001b[38;5;66;03m# \"Union[ExtensionArray, ndarray[Any, Any]]\"; expected \"ndarray[Any, Any]\"\u001b[39;00m\n\u001b[0;32m--> 283\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[43m_na_arithmetic_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res_values\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py:227\u001b[0m, in \u001b[0;36m_na_arithmetic_op\u001b[0;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_cmp \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[1;32m 221\u001b[0m left\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(right, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m\n\u001b[1;32m 222\u001b[0m ):\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# Don't do this for comparisons, as that will handle complex numbers\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# incorrectly, see GH#32047\u001b[39;00m\n\u001b[0;32m--> 227\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_masked_arith_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m/venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py:163\u001b[0m, in \u001b[0;36m_masked_arith_op\u001b[0;34m(x, y, op)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;66;03m# See GH#5284, GH#5035, GH#19448 for historical reference\u001b[39;00m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m--> 163\u001b[0m result[mask] \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxrav\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myrav\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(y):\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'str'" + ] + } + ], + "source": [ + "df[\"price_ratio\"] = df[\"pricing_completion\"] / df[\"pricing_prompt\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"total_price\"] = " + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/helpers/notebooks/hopenai_tutorial.py b/helpers/notebooks/hopenai_tutorial.py new file mode 100644 index 000000000..bc2c943b1 --- /dev/null +++ b/helpers/notebooks/hopenai_tutorial.py @@ -0,0 +1,133 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.16.7 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% [markdown] +# CONTENTS: +# - [Description](#description) + +# %% [markdown] +# +# # Description +# +# This notebook examines ... + +# %% +# #!sudo /bin/bash -c "(source /venv/bin/activate; pip install --quiet jupyterlab-vim)" +# #!jupyter labextension enable + +# %% +# %load_ext autoreload +# %autoreload 2 + +import logging + +import helpers.hdbg as hdbg +import helpers.henv as henv +import helpers.hprint as hprint + +# %% +print(henv.get_system_signature()[0]) + +hprint.config_notebook() + +# %% +# hdbg.init_logger(verbosity=logging.DEBUG) +hdbg.init_logger(verbosity=logging.INFO) +# hdbg.test_logger() +_LOG = logging.getLogger(__name__) + +# %% +# !sudo /bin/bash -c "(source /venv/bin/activate; pip install --quiet openai requests)" + +# %% +import helpers.hopenai as hopenai + +# %% +val = hopenai.get_model_stats() + +# %% +import pprint +pprint.pprint(val[0]) + +# %% +import pandas as pd +import numpy as np + +# %% +# Normalize the nested JSON +df = pd.json_normalize(val, sep='_') +df.set_index("id", inplace=True) +# View the resulting DataFrame +#print(df.T) # Transpose just for readable vertical inspection + +# %% +df.iloc[0].T + +# %% +df.dtypes + +# %% +import helpers.hpandas as hpandas + +# %% +report_invalid_values = True +df_out, types_df = hpandas.infer_types_df(df, report_invalid_values=report_invalid_values) + +#df_out.head() + +# %% +df["top_provider_max_completion_tokens"].isna().sum() + +# %% +types_df + +# %% [markdown] +# # + +# %% +for col in df.columns: + hpandas.infer_column_types(df[col]) + +# %% +df.apply(lambda x: pd.Series(infer_column_types(x))).T + +# %% +hopenai.infer_column_types_df(df) + +# %% +a = pd.Series([0, "hello", None]) +b = pd.to_numeric(a, errors="coerce") +#b.apply(lambda x: float(x).is_integer()) +b.astype(int) + +# %% +pd.to_numeric(df["pricing_request"], errors='coerce').notna() + +# %% +df["pricing_completion"] + +# %% +df.sort_values("pricing_prompt")[col_names] + +# %% +df[["pricing_prompt", "pricing_completion"]].plot.scatter(x="pricing_prompt", y="pricing_completion") + +# %% +df["price_ratio"] = df["pricing_completion"] / df["pricing_prompt"] + +# %% + +# %% +#df["total_price"] =