# Documentation

## Docs

- [embed_dataset](https://docs.graphext.com/api-docs/analyse/embed/embed_dataset.md): Reduce the dataset to an n-dimensional numeric vector embedding.
- [embed_images](https://docs.graphext.com/api-docs/analyse/embed/embed_images.md): Embed images using pretrained DL models.
- [embed_items](https://docs.graphext.com/api-docs/analyse/embed/embed_items.md): Trains an _item2vec_ model on provided lists of items (or sentences of words, etc.).
- [embed_sessions](https://docs.graphext.com/api-docs/analyse/embed/embed_sessions.md): Trains an _item2vec_ model on provided lists of items.
- [embed_text](https://docs.graphext.com/api-docs/analyse/embed/embed_text.md): Parse and calculate a (word-averaged) embedding vector for each text.
- [embed_text_with_model](https://docs.graphext.com/api-docs/analyse/embed/embed_text_with_model.md): Use language models to calulate an embedding for each text in provided column.
- [embed_with_trees](https://docs.graphext.com/api-docs/analyse/embed/embed_with_trees.md): Reduce the dataset to an n-dimensional numeric vector embedding using a Forest model's tree indices.
- [Embed](https://docs.graphext.com/api-docs/analyse/embed/index.md)
- [layout_dataset](https://docs.graphext.com/api-docs/analyse/embed/layout_dataset.md): Reduce the dataset to 2 dimensions that can be mapped to x/y node positions.
- [vectorize_dataset](https://docs.graphext.com/api-docs/analyse/embed/vectorize_dataset.md): Create a vectorized (numeric) dataset, (optionally) of reduced dimensionality.
- [association_rules](https://docs.graphext.com/api-docs/analyse/graph_and_map/association_rules.md): Calculate association rules for a items/products in a dataset of transactions.
- [cluster_dataset](https://docs.graphext.com/api-docs/analyse/graph_and_map/cluster_dataset.md): Identify clusters in the dataset.
- [cluster_embeddings](https://docs.graphext.com/api-docs/analyse/graph_and_map/cluster_embeddings.md): Identify clusters using the distance between provided embeddings.
- [cluster_network](https://docs.graphext.com/api-docs/analyse/graph_and_map/cluster_network.md): Identify clusters in the network.
- [cluster_subnetwork](https://docs.graphext.com/api-docs/analyse/graph_and_map/cluster_subnetwork.md): Identify clusters in the network by filtering the input dataset.
- [extract_node_betweenness](https://docs.graphext.com/api-docs/analyse/graph_and_map/extract_node_betweenness.md): Calculate network node betweenness.
- [extract_node_closeness](https://docs.graphext.com/api-docs/analyse/graph_and_map/extract_node_closeness.md): Calculcate network node closeness.
- [extract_node_degree](https://docs.graphext.com/api-docs/analyse/graph_and_map/extract_node_degree.md): Calculate network node degrees.
- [extract_node_pagerank](https://docs.graphext.com/api-docs/analyse/graph_and_map/extract_node_pagerank.md): Calculate network node pagerank.
- [Graph And Map](https://docs.graphext.com/api-docs/analyse/graph_and_map/index.md)
- [layout_coordinates](https://docs.graphext.com/api-docs/analyse/graph_and_map/layout_coordinates.md): Create x, y positions for nodes from their geographical coordinates.
- [layout_dataset](https://docs.graphext.com/api-docs/analyse/graph_and_map/layout_dataset.md): Reduce the dataset to 2 dimensions that can be mapped to x/y node positions.
- [layout_igraph](https://docs.graphext.com/api-docs/analyse/graph_and_map/layout_igraph.md): Calculate layout, i.e. node positions, for a network.
- [layout_network](https://docs.graphext.com/api-docs/analyse/graph_and_map/layout_network.md): Compute a force-directed graph layout with a fast forceAtlas2 implementation.
- [layout_treemap](https://docs.graphext.com/api-docs/analyse/graph_and_map/layout_treemap.md): Place nodes on the screen using a treemap layout.
- [link_embeddings](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_embeddings.md): Create network links between rows/nodes calculating the similarity of embeddings (vectors).
- [link_grouped_embeddings](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_grouped_embeddings.md): Create network links calculating the similarity of embeddings (vectors) within groups.
- [link_rows](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_rows.md): Create network links using explicit lists of target IDs, weights and other link attributes.
- [link_rows_by_id](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_rows_by_id.md): Create network links using one or more lists of target ids.
- [link_rows_by_rownum](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_rows_by_rownum.md): Create network links using explicit lists of target row numbers and optional weights.
- [link_sequence_items](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_sequence_items.md): Create network links between consecutive pairs in a column of sequences.
- [link_session_items](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_session_items.md): Link items (e.g. products) in sessions (baskets) if one item makes the presence of the other in  the same session more likely.
- [link_similar_columns](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_similar_columns.md): Calculates all pair-wise column dependencies (by default mutual information).
- [link_similar_rows](https://docs.graphext.com/api-docs/analyse/graph_and_map/link_similar_rows.md): Create network links calculating similarity between multidimensional and multitype documents.
- [merge_links](https://docs.graphext.com/api-docs/analyse/graph_and_map/merge_links.md): Merge multiple sets of network link columns into a single unified link set.
- [caption_images](https://docs.graphext.com/api-docs/analyse/infer/caption_images.md): Predict image captions using pretrained DL models.
- [classify_text](https://docs.graphext.com/api-docs/analyse/infer/classify_text.md): Classify texts using any model from the [Hugging Face hub](https://huggingface.co/models).
- [embed_images](https://docs.graphext.com/api-docs/analyse/infer/embed_images.md): Embed images using pretrained DL models.
- [embed_text_with_model](https://docs.graphext.com/api-docs/analyse/infer/embed_text_with_model.md): Use language models to calulate an embedding for each text in provided column.
- [Infer](https://docs.graphext.com/api-docs/analyse/infer/index.md)
- [prompt_ai](https://docs.graphext.com/api-docs/analyse/infer/prompt_ai.md): Call OpenAI's models on each row of the dataset for a given prompt.
- [zeroshot_classify_text](https://docs.graphext.com/api-docs/analyse/infer/zeroshot_classify_text.md): Classify texts using custom labels/categories.
- [calibrate_classification](https://docs.graphext.com/api-docs/analyse/train_and_predict/calibrate_classification.md): Calibrate a classification model.
- [explain_predictions](https://docs.graphext.com/api-docs/analyse/train_and_predict/explain_predictions.md): Explain a prediction model.
- [Train And Predict](https://docs.graphext.com/api-docs/analyse/train_and_predict/index.md)
- [predict_classification](https://docs.graphext.com/api-docs/analyse/train_and_predict/predict_classification.md): Use a pretrained classification model to predict new categorical data.
- [predict_clustering](https://docs.graphext.com/api-docs/analyse/train_and_predict/predict_clustering.md): Use a pretrained clustering model to predict new data.
- [predict_dimensionality_reduction](https://docs.graphext.com/api-docs/analyse/train_and_predict/predict_dimensionality_reduction.md): Use a pretrained model to predict embeddings.
- [predict_regression](https://docs.graphext.com/api-docs/analyse/train_and_predict/predict_regression.md): Use a pretrained model to predict new numerical data.
- [predict_survival](https://docs.graphext.com/api-docs/analyse/train_and_predict/predict_survival.md): Use a pretrained model to predict new data.
- [test_classification](https://docs.graphext.com/api-docs/analyse/train_and_predict/test_classification.md): Evaluate a pretrained classification model on custom test data.
- [test_classification_gpu](https://docs.graphext.com/api-docs/analyse/train_and_predict/test_classification_gpu.md): Evaluate a pretrained classification model on custom test data.
- [test_regression](https://docs.graphext.com/api-docs/analyse/train_and_predict/test_regression.md): Evaluate a pretrained regression model on custom test data.
- [train_classification](https://docs.graphext.com/api-docs/analyse/train_and_predict/train_classification.md): Train and store a classification model to be loaded at a later point for prediction.
- [train_classification_gpu](https://docs.graphext.com/api-docs/analyse/train_and_predict/train_classification_gpu.md): Train and store a classification model to be loaded at a later point for prediction.
- [train_clustering](https://docs.graphext.com/api-docs/analyse/train_and_predict/train_clustering.md): Train and store a machine learning model to be loaded at a later point for prediction.
- [train_dimensionality_reduction](https://docs.graphext.com/api-docs/analyse/train_and_predict/train_dimensionality_reduction.md): Train and store a machine learning model to be loaded at a later point for prediction.
- [train_regression](https://docs.graphext.com/api-docs/analyse/train_and_predict/train_regression.md): Train and store a regression model to be loaded at a later point for prediction.
- [train_survival](https://docs.graphext.com/api-docs/analyse/train_and_predict/train_survival.md): Train and store a survival model to be loaded at a later point for prediction.
- [aggregate](https://docs.graphext.com/api-docs/prepare/aggregate/aggregate.md): Group and aggregate a dataset using any of a number of predefined functions.
- [aggregate_list_items](https://docs.graphext.com/api-docs/prepare/aggregate/aggregate_list_items.md): Group a dataset by elements in a column of lists and aggregate remaining columns using one or more predefined functions.
- [aggregate_neighbours](https://docs.graphext.com/api-docs/prepare/aggregate/aggregate_neighbours.md): For each node in a network, group and aggregate over its neighbours.
- [aggregate_tweets_by_author](https://docs.graphext.com/api-docs/prepare/aggregate/aggregate_tweets_by_author.md): Group a dataset of tweets by author and calculate relevant author statistics.
- [featurize_time_series](https://docs.graphext.com/api-docs/prepare/aggregate/featurize_time_series.md): Summarizes time series data into aggregate metrics.
- [group_by](https://docs.graphext.com/api-docs/prepare/aggregate/group_by.md): Group data by specified columns and apply aggregation functions to each group.
- [Aggregate](https://docs.graphext.com/api-docs/prepare/aggregate/index.md)
- [melt](https://docs.graphext.com/api-docs/prepare/aggregate/melt.md): Reshape a dataset by transforming columns into rows.
- [resample](https://docs.graphext.com/api-docs/prepare/aggregate/resample.md): Resamples a dataset of events or time series to the desired frequency.
- [caption_images](https://docs.graphext.com/api-docs/prepare/enrich/caption_images.md): Predict image captions using pretrained DL models.
- [classify_text](https://docs.graphext.com/api-docs/prepare/enrich/classify_text.md): Classify texts using any model from the [Hugging Face hub](https://huggingface.co/models).
- [clean_categories](https://docs.graphext.com/api-docs/prepare/enrich/clean_categories.md): Clean a given column of categories or lists of categories using OpenAI.
- [describe_clusters](https://docs.graphext.com/api-docs/prepare/enrich/describe_clusters.md): Describe your clusters from their given relevant TF-IDF terms using OpenAI.
- [explore_database](https://docs.graphext.com/api-docs/prepare/enrich/explore_database.md): Explore database structure — generates a dataset of column metadata with statistics, relationships, and semantic descriptions.
- [fetch_demographics_es](https://docs.graphext.com/api-docs/prepare/enrich/fetch_demographics_es.md): Fetch Spanish demographic census data given a geographical location in each row.
- [fetch_full_contact_domains](https://docs.graphext.com/api-docs/prepare/enrich/fetch_full_contact_domains.md): Enrich a dataset containing links (URLs) to companies' online presence using the FullContact service.
- [fetch_full_contact_emails](https://docs.graphext.com/api-docs/prepare/enrich/fetch_full_contact_emails.md): Enrich a dataset containing email addresses with personal information using the _FullContact_ service.
- [fetch_google_places](https://docs.graphext.com/api-docs/prepare/enrich/fetch_google_places.md): Fetch information about the most relevant places surrounding a location.
- [fetch_google_vision](https://docs.graphext.com/api-docs/prepare/enrich/fetch_google_vision.md): Analyze images given their URL using the Google Vision API.
- [fetch_location](https://docs.graphext.com/api-docs/prepare/enrich/fetch_location.md): Extract formatted address, locality, area, state, country and geographical coordinates from one or more address columns.
- [fetch_openreview](https://docs.graphext.com/api-docs/prepare/enrich/fetch_openreview.md): Fetch publications submitted to one or more conferences via [OpenReview](https://openreview.net/).
- [fetch_social_shares](https://docs.graphext.com/api-docs/prepare/enrich/fetch_social_shares.md): Fetch the number of times a Url was shared on Facebook.
- [fetch_twitter](https://docs.graphext.com/api-docs/prepare/enrich/fetch_twitter.md): Enriches a dataset containing tweets with information about their authors.
- [fetch_twitter_api_io](https://docs.graphext.com/api-docs/prepare/enrich/fetch_twitter_api_io.md): Fetch tweets from Twitter/X using TwitterAPI.io advanced search.
- [fetch_url_content](https://docs.graphext.com/api-docs/prepare/enrich/fetch_url_content.md): Fetch the main text from a web URL, and return its title, author, content, excerpt and domain.
- [Enrich](https://docs.graphext.com/api-docs/prepare/enrich/index.md)
- [infer_aspect_polarity](https://docs.graphext.com/api-docs/prepare/enrich/infer_aspect_polarity.md): Extract aspect-based polarity from texts using OpenAI. Identifies entities mentioned in texts and separates them into positive and negative aspects based on expressed polarity. Returns two columns: one containing positively-mentioned entities and another containing negatively-mentioned entities. Opt…
- [infer_gender](https://docs.graphext.com/api-docs/prepare/enrich/infer_gender.md): Try to infer a person's gender given a first name.
- [infer_language](https://docs.graphext.com/api-docs/prepare/enrich/infer_language.md): Detect the language used for each text in the input column.
- [infer_missing](https://docs.graphext.com/api-docs/prepare/enrich/infer_missing.md): Train and use a machine learning model to predict (impute) the missing values in a column.
- [infer_missing_with_probs](https://docs.graphext.com/api-docs/prepare/enrich/infer_missing_with_probs.md): Train and use a machine learning model to predict (impute) the missing values in a column.
- [infer_sentiment](https://docs.graphext.com/api-docs/prepare/enrich/infer_sentiment.md): Parse text and calculate the overall positive or negative sentiment polarity.
- [infer_topics](https://docs.graphext.com/api-docs/prepare/enrich/infer_topics.md): Generate topics and subtopics for given texts using OpenAI.
- [prompt_ai](https://docs.graphext.com/api-docs/prepare/enrich/prompt_ai.md): Call OpenAI's models on each row of the dataset for a given prompt.
- [zeroshot_classify_text](https://docs.graphext.com/api-docs/prepare/enrich/zeroshot_classify_text.md): Classify texts using custom labels/categories.
- [filter_containing](https://docs.graphext.com/api-docs/prepare/filter/filter_containing.md): Filter rows containing any or all of a number of specified values.
- [filter_duplicate_nodes](https://docs.graphext.com/api-docs/prepare/filter/filter_duplicate_nodes.md): Remove duplicate nodes in a network.
- [filter_duplicates](https://docs.graphext.com/api-docs/prepare/filter/filter_duplicates.md): Filter duplicate rows, keeping the first or last of each set of duplicates found only.
- [filter_missing](https://docs.graphext.com/api-docs/prepare/filter/filter_missing.md): Filter rows based on missing values in one or more columns.
- [filter_range](https://docs.graphext.com/api-docs/prepare/filter/filter_range.md): Filter rows based on the numeric values in a given column.
- [filter_row_numbers](https://docs.graphext.com/api-docs/prepare/filter/filter_row_numbers.md): Filter rows by row number.
- [filter_rows](https://docs.graphext.com/api-docs/prepare/filter/filter_rows.md): Filter rows using graphext's advanced query syntax (similar to Elasticsearch).
- [filter_sample](https://docs.graphext.com/api-docs/prepare/filter/filter_sample.md): Randomly sample the dataset, optionally within groups (can be used to balance a dataset).
- [filter_topn](https://docs.graphext.com/api-docs/prepare/filter/filter_topn.md): Sort a dataset by selected columns and pick the first N rows (or exclude them).
- [filter_values](https://docs.graphext.com/api-docs/prepare/filter/filter_values.md): Filter rows where column matches specified values exactly.
- [filter_with_formula](https://docs.graphext.com/api-docs/prepare/filter/filter_with_formula.md): Filter rows using a (pandas-compatible) formula.
- [Filter](https://docs.graphext.com/api-docs/prepare/filter/index.md)
- [upsample](https://docs.graphext.com/api-docs/prepare/filter/upsample.md): Upsample a dataset given a weight column.
- [append_rows](https://docs.graphext.com/api-docs/prepare/join_and_combine/append_rows.md): Add rows from one dataset to another.
- [Join And Combine](https://docs.graphext.com/api-docs/prepare/join_and_combine/index.md)
- [join](https://docs.graphext.com/api-docs/prepare/join_and_combine/join.md): Join two datasets on their row indexes or on values in specified columns.
- [add_noise](https://docs.graphext.com/api-docs/prepare/transform/add_noise.md): Add noise to a column with numbers or lists of numbers.
- [calculate](https://docs.graphext.com/api-docs/prepare/transform/calculate.md): Evaluates a formula containing basic arithmetic over a dataset's columns.
- [cast](https://docs.graphext.com/api-docs/prepare/transform/cast.md): Interprets and changes a column's data to another (semantic) type.
- [concatenate](https://docs.graphext.com/api-docs/prepare/transform/concatenate.md): Concatenate columns as text or lists with optional separator as well as pre- and postfix.
- [count_unique](https://docs.graphext.com/api-docs/prepare/transform/count_unique.md): Counts the number of unique elements in each list/array of the input column.
- [derive_column](https://docs.graphext.com/api-docs/prepare/transform/derive_column.md): Derive a new column with a custom JS script.
- [discretize_on_quantiles](https://docs.graphext.com/api-docs/prepare/transform/discretize_on_quantiles.md): Discretize column into bins based on quantiles.
- [discretize_on_values](https://docs.graphext.com/api-docs/prepare/transform/discretize_on_values.md): Discretize column by binning its values using explicitly specified cuts points.
- [divide](https://docs.graphext.com/api-docs/prepare/transform/divide.md): Divide two or more numeric columns in given order.
- [equal](https://docs.graphext.com/api-docs/prepare/transform/equal.md): Check the row-wise equality of all input columns.
- [explode](https://docs.graphext.com/api-docs/prepare/transform/explode.md): Explode (extract) items from column(s) of lists into separate rows.
- [extract_date_component](https://docs.graphext.com/api-docs/prepare/transform/extract_date_component.md): Extract a component such as day, week, weekday etc. from a date column.
- [extract_emoji](https://docs.graphext.com/api-docs/prepare/transform/extract_emoji.md): Parse texts and extract their emoji.
- [extract_entities](https://docs.graphext.com/api-docs/prepare/transform/extract_entities.md): Parse texts and extract the entities mentioned (persons, organizations etc.).
- [extract_hashtags](https://docs.graphext.com/api-docs/prepare/transform/extract_hashtags.md): Parse texts and extract any hashtags mentioned.
- [extract_json_values](https://docs.graphext.com/api-docs/prepare/transform/extract_json_values.md): Extract values from JSON columns using JsonPath.
- [extract_keywords](https://docs.graphext.com/api-docs/prepare/transform/extract_keywords.md): Parse and extract keywords from texts.
- [extract_mentions](https://docs.graphext.com/api-docs/prepare/transform/extract_mentions.md): Parse texts and extract any mentions detected.
- [extract_ngrams](https://docs.graphext.com/api-docs/prepare/transform/extract_ngrams.md): Parse texts and extract their n-grams.
- [extract_range](https://docs.graphext.com/api-docs/prepare/transform/extract_range.md): Create a copy of a column nullifying values outside a specified range.
- [extract_regex](https://docs.graphext.com/api-docs/prepare/transform/extract_regex.md): Extract parts of texts detected using regular expressions.
- [extract_text_features](https://docs.graphext.com/api-docs/prepare/transform/extract_text_features.md): Parse and process texts to extract multiple features at once.
- [extract_url_components](https://docs.graphext.com/api-docs/prepare/transform/extract_url_components.md): Extract components from an URL.
- [Transform](https://docs.graphext.com/api-docs/prepare/transform/index.md)
- [is_missing](https://docs.graphext.com/api-docs/prepare/transform/is_missing.md): Check for missing values in a given column.
- [label_bios](https://docs.graphext.com/api-docs/prepare/transform/label_bios.md): Categorize people into fields of occupation using their bios (biographies).
- [label_categories](https://docs.graphext.com/api-docs/prepare/transform/label_categories.md): Relabel categories based on the top terms in each category.
- [label_encode](https://docs.graphext.com/api-docs/prepare/transform/label_encode.md): Encode categories with values between 0 and N-1, where N is the number of unique categories.
- [label_holidays](https://docs.graphext.com/api-docs/prepare/transform/label_holidays.md): Indicate if there are any holidays for given date, location pairs.
- [label_political_subtopics](https://docs.graphext.com/api-docs/prepare/transform/label_political_subtopics.md): Categorize the political sub-topics of texts in Spanish.
- [label_political_topics](https://docs.graphext.com/api-docs/prepare/transform/label_political_topics.md): Categorize the political topics of texts in Spanish.
- [label_texts_containing](https://docs.graphext.com/api-docs/prepare/transform/label_texts_containing.md): Categorize texts containing specific keywords with custom labels.
- [label_texts_containing_from_query](https://docs.graphext.com/api-docs/prepare/transform/label_texts_containing_from_query.md): Label texts given an elastic-like query string.
- [length](https://docs.graphext.com/api-docs/prepare/transform/length.md): Calculates the length of lists (number of elements) or texts/categories (number of characters).
- [make_constant](https://docs.graphext.com/api-docs/prepare/transform/make_constant.md): Creates a new constant column (with a single unique value) of the same length as the input column.
- [math_func](https://docs.graphext.com/api-docs/prepare/transform/math_func.md): Applies a mathematical function to the values of a (single) numeric column.
- [merge_similar_semantics](https://docs.graphext.com/api-docs/prepare/transform/merge_similar_semantics.md): Group categories with similar meanings.
- [merge_similar_spellings](https://docs.graphext.com/api-docs/prepare/transform/merge_similar_spellings.md): Group categories with similar spellings.
- [multiply](https://docs.graphext.com/api-docs/prepare/transform/multiply.md): Multiply two or more numeric columns.
- [normalize](https://docs.graphext.com/api-docs/prepare/transform/normalize.md): Normalizes a numerical column by subtracting the mean and dividing by its standard deviation.
- [observed_duration](https://docs.graphext.com/api-docs/prepare/transform/observed_duration.md): Calculate the duration between two dates and determine whether an event was observed before a specified observation date.
- [order_categories](https://docs.graphext.com/api-docs/prepare/transform/order_categories.md): (Re-)order the categories of a categorical column.
- [pandas_func](https://docs.graphext.com/api-docs/prepare/transform/pandas_func.md): Applies an arbitrary pandas supported function to the values of an input column.
- [pct_change](https://docs.graphext.com/api-docs/prepare/transform/pct_change.md): Calculate percentage change between consecutive numbers in a numeric column.
- [percentile_rank](https://docs.graphext.com/api-docs/prepare/transform/percentile_rank.md): Convert the values in a numeric or date column into their percentile rank.
- [query](https://docs.graphext.com/api-docs/prepare/transform/query.md): Generate a boolean column based on a query string, marking rows that match the condition.
- [replace_missing](https://docs.graphext.com/api-docs/prepare/transform/replace_missing.md): Replace missing values (NaNs) with either a specified constant value or the result of a given function.
- [replace_regex](https://docs.graphext.com/api-docs/prepare/transform/replace_regex.md): Replace parts of text detected with a regular expression.
- [replace_values](https://docs.graphext.com/api-docs/prepare/transform/replace_values.md): Replace specified values in a column with new ones.
- [scale](https://docs.graphext.com/api-docs/prepare/transform/scale.md): Scales the values of a numerical column to lie between a specified minimum and maximum.
- [segment_rows](https://docs.graphext.com/api-docs/prepare/transform/segment_rows.md): Create a segmentation using graphext's advanced query syntax (similar to Elasticsearch).
- [slice](https://docs.graphext.com/api-docs/prepare/transform/slice.md): Extract a range/slice of elements from a column of texts or lists.
- [split_string](https://docs.graphext.com/api-docs/prepare/transform/split_string.md): Split a single column containing texts into two.
- [subtract](https://docs.graphext.com/api-docs/prepare/transform/subtract.md): Subtract two or more numeric columns.
- [sum](https://docs.graphext.com/api-docs/prepare/transform/sum.md): Calculate the row-wise sum of numeric columns.
- [time_interval](https://docs.graphext.com/api-docs/prepare/transform/time_interval.md): Calculates the duration of a time interval between two dates (datetimes/timestamps).
- [tokenize](https://docs.graphext.com/api-docs/prepare/transform/tokenize.md): Parse texts and separate them into lists of tokens (words, lemmas, etc.).
- [trim_frequencies](https://docs.graphext.com/api-docs/prepare/transform/trim_frequencies.md): Remove values whose frequencies (counts) are above/below a given threshold.
- [unique](https://docs.graphext.com/api-docs/prepare/transform/unique.md): Extracts the unique elements in each list/array.
- [unpack_list](https://docs.graphext.com/api-docs/prepare/transform/unpack_list.md): Unpack (extract) items from a column of lists into separate columns.
- [configure_category_colors](https://docs.graphext.com/api-docs/report/configure_ui/configure_category_colors.md): Configures the color of the categories of a categorical or text column.
- [configure_category_labels](https://docs.graphext.com/api-docs/report/configure_ui/configure_category_labels.md): Configures the labels generated for each category.
- [configure_category_order](https://docs.graphext.com/api-docs/report/configure_ui/configure_category_order.md): Configures the order of categories in a categorical or list of categories column.
- [configure_color_palette](https://docs.graphext.com/api-docs/report/configure_ui/configure_color_palette.md): Configures the base global color palettes to use when coloring categorical or quantitative columns.
- [configure_column_metadata](https://docs.graphext.com/api-docs/report/configure_ui/configure_column_metadata.md): Configures the label and/or description of a column.
- [configure_column_view_modes](https://docs.graphext.com/api-docs/report/configure_ui/configure_column_view_modes.md): Configures the visualization mode for columns in the filters panel.
- [configure_column_visibility](https://docs.graphext.com/api-docs/report/configure_ui/configure_column_visibility.md): Configures the visibility of a column in different Graphext sections.
- [configure_columns_order](https://docs.graphext.com/api-docs/report/configure_ui/configure_columns_order.md): Configures the order of columns (filters) in the Graph and Details sections.
- [configure_dataset_metadata](https://docs.graphext.com/api-docs/report/configure_ui/configure_dataset_metadata.md): Configures the info_source, label and/or description of a dataset.
- [configure_detail_view](https://docs.graphext.com/api-docs/report/configure_ui/configure_detail_view.md): Select the preferred columns to customize a row detail view.
- [configure_discarded_categories](https://docs.graphext.com/api-docs/report/configure_ui/configure_discarded_categories.md): Configures a minimum number of rows in a category below which the category will be hidden from the variable's filter view.
- [configure_graph_layout](https://docs.graphext.com/api-docs/report/configure_ui/configure_graph_layout.md): Configures the x & y columns used to map node positions in the graph.
- [configure_graph_regions](https://docs.graphext.com/api-docs/report/configure_ui/configure_graph_regions.md): Configures the column that is displayed as the label of the graph region.
- [configure_metrics](https://docs.graphext.com/api-docs/report/configure_ui/configure_metrics.md): Configures the metrics to be calculated and displayed.
- [configure_node_color](https://docs.graphext.com/api-docs/report/configure_ui/configure_node_color.md): Configures the column that is used for coloring the nodes by default.
- [configure_node_connections](https://docs.graphext.com/api-docs/report/configure_ui/configure_node_connections.md): Configures how the connections between the nodes are visualized.
- [configure_node_picture](https://docs.graphext.com/api-docs/report/configure_ui/configure_node_picture.md): Configures the pictures associated with the nodes of the network.
- [configure_node_size](https://docs.graphext.com/api-docs/report/configure_ui/configure_node_size.md): Configures the column, the minimum and the maximum that are used for sizing the nodes by default.
- [configure_node_title](https://docs.graphext.com/api-docs/report/configure_ui/configure_node_title.md): Configures the column that is displayed as the title of the node.
- [configure_node_url](https://docs.graphext.com/api-docs/report/configure_ui/configure_node_url.md): Configures the urls associated with the nodes of the network.
- [configure_rows_order](https://docs.graphext.com/api-docs/report/configure_ui/configure_rows_order.md): Configures the order of the rows in the Data section.
- [configure_sections](https://docs.graphext.com/api-docs/report/configure_ui/configure_sections.md): Configures pinned Graphext sections.
- [configure_tagged_columns](https://docs.graphext.com/api-docs/report/configure_ui/configure_tagged_columns.md): Create groups of variables by tagging the provided variable(s) with the specified tag.
- [Configure UI](https://docs.graphext.com/api-docs/report/configure_ui/index.md)
- [create_compare_insight](https://docs.graphext.com/api-docs/report/create_insight/create_compare_insight.md): Create a new insight from the Compare section.
- [create_correlations_insight](https://docs.graphext.com/api-docs/report/create_insight/create_correlations_insight.md): Create a new insight from the Correlations section.
- [create_filter_insight](https://docs.graphext.com/api-docs/report/create_insight/create_filter_insight.md): Create a new insight from a selection of nodes.
- [create_graph_insight](https://docs.graphext.com/api-docs/report/create_insight/create_graph_insight.md): Create a new insight from the Graph section.
- [create_plot_insight](https://docs.graphext.com/api-docs/report/create_insight/create_plot_insight.md): Create a new insight from the Plot section.
- [create_text_insight](https://docs.graphext.com/api-docs/report/create_insight/create_text_insight.md): Create a new insight using only plain text.
- [Create Insight](https://docs.graphext.com/api-docs/report/create_insight/index.md)
- [create_project](https://docs.graphext.com/api-docs/report/create_project/create_project.md): Prepare project using the final dataset.
- [Create Project](https://docs.graphext.com/api-docs/report/create_project/index.md)
- [export_to_airtable](https://docs.graphext.com/api-docs/report/export/export_to_airtable.md): Export data to Airtable.
- [export_to_amazonredshift](https://docs.graphext.com/api-docs/report/export/export_to_amazonredshift.md): Export data to Amazon Redshift.
- [export_to_amazons3](https://docs.graphext.com/api-docs/report/export/export_to_amazons3.md): Export data to an AmazonS3 bucket.
- [export_to_azureblob](https://docs.graphext.com/api-docs/report/export/export_to_azureblob.md): Export data to an Azure Storage Blob.
- [export_to_azuresql](https://docs.graphext.com/api-docs/report/export/export_to_azuresql.md): Export data to Azure SQL.
- [export_to_bigquery](https://docs.graphext.com/api-docs/report/export/export_to_bigquery.md): Export data to a BigQuery Table.
- [export_to_databricks](https://docs.graphext.com/api-docs/report/export/export_to_databricks.md): Export data to Databricks.
- [export_to_gdrive](https://docs.graphext.com/api-docs/report/export/export_to_gdrive.md): Export data to a Google Drive file.
- [export_to_gsheet](https://docs.graphext.com/api-docs/report/export/export_to_gsheet.md): Export data to a Google Sheets sheet.
- [export_to_notion](https://docs.graphext.com/api-docs/report/export/export_to_notion.md): Export data to Notion.
- [export_to_snowflake](https://docs.graphext.com/api-docs/report/export/export_to_snowflake.md): Export data to Snowflake.
- [export_to_sql](https://docs.graphext.com/api-docs/report/export/export_to_sql.md): Export a given dataset to a specified SQL database.
- [export_to_tinybird](https://docs.graphext.com/api-docs/report/export/export_to_tinybird.md): Export data to Tinybird.
- [Export](https://docs.graphext.com/api-docs/report/export/index.md)
- [Recipe steps](https://docs.graphext.com/api-docs/steps.md): Working with data in Graphext's low-code mode
- [Classification model](https://docs.graphext.com/concepts/ds-concepts/classification-model.md): Start exploring your data and discovering insights in under 5 minutes
- [Correlation](https://docs.graphext.com/concepts/ds-concepts/correlation.md): Start exploring your data and discovering insights in under 5 minutes
- [Advanced Filter Queries](https://docs.graphext.com/concepts/ds-concepts/filter-queries.md)
- [Graphs and layouts](https://docs.graphext.com/concepts/ds-concepts/graphs.md)
- [Graphs and data types](https://docs.graphext.com/concepts/ds-concepts/graphs-and-data-types.md)
- [Model training and evaluation](https://docs.graphext.com/concepts/ds-concepts/model-training-evaluation.md)
- [Regression model](https://docs.graphext.com/concepts/ds-concepts/regression-model.md): Start exploring your data and discovering insights in under 5 minutes
- [UMAP](https://docs.graphext.com/concepts/ds-concepts/umap.md): Start exploring your data and discovering insights in under 5 minutes
- [Cross Filters](https://docs.graphext.com/concepts/graphext-concepts/cross-filters.md): Quickly filter and select data
- [Insights](https://docs.graphext.com/concepts/graphext-concepts/insights.md): Save and recall findings in your research
- [Recipe](https://docs.graphext.com/concepts/graphext-concepts/recipe.md): The recipe is the backbone of any Graphext proyect
- [Segments](https://docs.graphext.com/concepts/graphext-concepts/segments.md): Group selections for easy access
- [Significant variables](https://docs.graphext.com/concepts/graphext-concepts/significant-variables.md): A quick peek into potential correlations
- [Steps](https://docs.graphext.com/concepts/graphext-concepts/steps.md): Steps are functions used in the context of the recipe
- [Tags](https://docs.graphext.com/concepts/graphext-concepts/tags.md): Make sense of your variables
- [Wizard](https://docs.graphext.com/concepts/graphext-concepts/wizard.md): Transform and enrich your data easily and conveniently
- [Advanced Filter Queries](https://docs.graphext.com/documentation/data-exploration/advanced-filter-queries.md): Surgically precise selection and filtering
- [The Compare tab](https://docs.graphext.com/documentation/data-exploration/compare.md): Explore your data in a massively parallel way
- [The Correlations tab](https://docs.graphext.com/documentation/data-exploration/correlations.md): Find out not only what, but why something happens
- [Advanced data selection](https://docs.graphext.com/documentation/data-exploration/cross-filters/advanced-selection.md): Rediscover data exploration interactively
- [Segments](https://docs.graphext.com/documentation/data-exploration/cross-filters/creating-segments.md): Group selections for easy access
- [Overview](https://docs.graphext.com/documentation/data-exploration/cross-filters/overview.md): Introducing Cross Filters
- [Significant Variables](https://docs.graphext.com/documentation/data-exploration/cross-filters/significant-variables.md): A quick peek into potential correlations
- [AI for data preparation](https://docs.graphext.com/documentation/data-preparation/ai-data-preparation.md): Start exploring your data and discovering insights in under 5 minutes
- [Data cleaning & transformation](https://docs.graphext.com/documentation/data-preparation/data-cleaning-transformation.md): Start exploring your data and discovering insights in under 5 minutes
- [Fill Missing Values](https://docs.graphext.com/documentation/data-preparation/data-enrichments/fill-missing-values.md): Use a function to fill missing values
- [Group Similar Semantics](https://docs.graphext.com/documentation/data-preparation/data-enrichments/group-similar-semantics.md): Group together words with similar meanings
- [Group Similar Spellings](https://docs.graphext.com/documentation/data-preparation/data-enrichments/group-similar-spellings.md): Group together similarly written words that may be spelled inconsistently
- [Infer Gender](https://docs.graphext.com/documentation/data-preparation/data-enrichments/infer-gender.md): Use the first names of people to make predictions about their gender
- [Overview](https://docs.graphext.com/documentation/data-preparation/data-enrichments/overview.md): Enhance your data easily and conveniently
- [Predict Missing Values](https://docs.graphext.com/documentation/data-preparation/data-enrichments/predict-missing-values.md): Train a model to infer missing values
- [Add Demographic Data for Spain using coordinates](https://docs.graphext.com/documentation/data-preparation/data-enrichments/spanish-demographic-coordinate-data.md): Use coordinates to enrich your data with the Spanish census information
- [Standardize Locations](https://docs.graphext.com/documentation/data-preparation/data-enrichments/standardize-locations.md): Group texts that refer to the same places
- [Upsample Survey Data](https://docs.graphext.com/documentation/data-preparation/data-enrichments/upsample-survey-data.md): Adjust the influence of individuals in your survey using a variable with predefined weights to scale your survey data.
- [Introduction to data preparation](https://docs.graphext.com/documentation/data-preparation/dp-introduction.md): Start exploring your data and discovering insights in under 5 minutes
- [Sampling data](https://docs.graphext.com/documentation/data-preparation/sampling-data.md): Start exploring your data and discovering insights in under 5 minutes
- [Finding data](https://docs.graphext.com/documentation/data-preparation/sampling-data/finding-data.md): Search for anything within your dataset
- [Sample data](https://docs.graphext.com/documentation/data-preparation/sampling-data/sample-data.md)
- [Dealing with nulls](https://docs.graphext.com/documentation/data-preparation/sampling-data/view-nulls.md): Get a quick overview of how much data you are missing
- [Add dataset information](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/add-dataset-description.md)
- [Adding variable descriptions](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/add-variable-description.md)
- [Arrange columns in the Data Table](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/arrange-columns-data-table.md)
- [Arrange variable order](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/arrange-variables-menu.md)
- [Change data table layout](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/change-data-table-layout.md)
- [Rename a variable](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/change-variable-name.md)
- [Add colors to the cross filters](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/color-filters.md)
- [Customizing statistics](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/customize-stats.md)
- [Delete a variable](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/delete-variable.md)
- [Editing segments](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/editing-segments.md)
- [Enable histograms in the table header](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/enable-histograms-header.md)
- [Freeze columns in the data table](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/freeze-columns-table.md)
- [Group variables using tags](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/group-variables.md)
- [Hide a variable](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/hide-variable.md)
- [Overview](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/overview.md): Essential tooling to move through Graphext
- [Pin variables](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/pin-variables-menu.md)
- [Sort the Data Table](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/sort-data-table.md)
- [Specifying order in categorical variables](https://docs.graphext.com/documentation/data-preparation/variable-management-ui-config/specify-order-in-column.md)
- [Choosing a chart](https://docs.graphext.com/documentation/data-visualization/choosing-a-chart.md)
- [Compare segments](https://docs.graphext.com/documentation/data-visualization/compare-segments.md): Start exploring your data and discovering insights in under 5 minutes
- [Correlations and patterns](https://docs.graphext.com/documentation/data-visualization/correlations-and-patterns.md): Start exploring your data and discovering insights in under 5 minutes
- [Create plots and charts](https://docs.graphext.com/documentation/data-visualization/create-a-plot.md): Create publish-ready charts in minutes
- [Customizing Axes](https://docs.graphext.com/documentation/data-visualization/customizing-axes.md): Get new perspectives on your data
- [Customizing Charts](https://docs.graphext.com/documentation/data-visualization/customizing-charts.md)
- [Exporting charts](https://docs.graphext.com/documentation/data-visualization/exporting-charts.md)
- [Area Chart](https://docs.graphext.com/documentation/data-visualization/types-of-chart/area-chart.md)
- [Bar Chart](https://docs.graphext.com/documentation/data-visualization/types-of-chart/bar-chart.md)
- [Box Plot](https://docs.graphext.com/documentation/data-visualization/types-of-chart/box-plot.md)
- [Heatmap](https://docs.graphext.com/documentation/data-visualization/types-of-chart/heatmap.md)
- [Line Chart](https://docs.graphext.com/documentation/data-visualization/types-of-chart/line-chart.md)
- [Overview](https://docs.graphext.com/documentation/data-visualization/types-of-chart/overview.md)
- [Scatter Plot](https://docs.graphext.com/documentation/data-visualization/types-of-chart/scatter-plot.md)
- [Graphext 101](https://docs.graphext.com/documentation/getting-started/quickstart.md): Get to know the tool with a practical example
- [What is Graphext?](https://docs.graphext.com/documentation/getting-started/what-is-graphext.md): The fastest visual analytics tool. Answer questions at the speed of thought.
- [Airtable](https://docs.graphext.com/documentation/import-and-export/data-connections/airtable.md): Connect Graphext to Airtable and be able to bring your data and write back to your data warehouse recurrently
- [Amazon S3](https://docs.graphext.com/documentation/import-and-export/data-connections/amazon-s3.md): Connect Graphext to Amazon S3 and be able to bring your data and write back recurrently
- [Redshift](https://docs.graphext.com/documentation/import-and-export/data-connections/aws-redshift.md): Connect Graphext to Amazon Redshift and be able to bring your data and write back to your data warehouse recurrently
- [Azure Blob Storage](https://docs.graphext.com/documentation/import-and-export/data-connections/azure-blob.md): Connect Graphext to Azure Blob Storage and be able to bring your data and write back recurrently
- [Azure SQL](https://docs.graphext.com/documentation/import-and-export/data-connections/azuresql.md): Connect Graphext to a Azure SQL and be able to bring your data and write back recurrently
- [Overview](https://docs.graphext.com/documentation/import-and-export/data-connections/data-connections.md): Get your data straight from the source
- [Databricks](https://docs.graphext.com/documentation/import-and-export/data-connections/databricks.md): Connect Graphext to Databricks and be able to bring your data and write back to your data warehouse recurrently
- [Google BigQuery](https://docs.graphext.com/documentation/import-and-export/data-connections/google-bigquery.md): Connect Graphext to Google BigQuery and be able to bring your data and write back to your data warehouse recurrently
- [Google Cloud Storage](https://docs.graphext.com/documentation/import-and-export/data-connections/google-cs.md): Connect Graphext to Google Cloud Storage and be able to bring your data and write back recurrently
- [Google Drive](https://docs.graphext.com/documentation/import-and-export/data-connections/google-drive.md): Connect Graphext to Google Drive and be able to bring your data and write back recurrently
- [Google Sheets](https://docs.graphext.com/documentation/import-and-export/data-connections/google-sheets.md): Connect Graphext to Google Sheets and be able to bring your data and write back recurrently
- [MySQL](https://docs.graphext.com/documentation/import-and-export/data-connections/mysql.md): Connect Graphext to a MySQL and be able to bring your data and write back recurrently
- [Notion](https://docs.graphext.com/documentation/import-and-export/data-connections/notion.md): Connect Graphext to Notion and be able to bring your data and write back to your data warehouse recurrently
- [Data Sources Connections](https://docs.graphext.com/documentation/import-and-export/data-connections/overview.md): Import and export from and to the most popular data sources
- [PostgreSQL](https://docs.graphext.com/documentation/import-and-export/data-connections/postgresql.md): Connect Graphext to a PostgreSQL and be able to bring your data and write back recurrently
- [Snowflake](https://docs.graphext.com/documentation/import-and-export/data-connections/snowflake.md): Connect Graphext to Snowflake and be able to bring your data and write back to your data warehouse recurrently
- [SQL Server](https://docs.graphext.com/documentation/import-and-export/data-connections/sqlserver.md): Connect Graphext to a SQL Server and be able to bring your data and write back recurrently
- [Tinybird](https://docs.graphext.com/documentation/import-and-export/data-connections/tinybird.md): Connect Graphext to Tinybird and be able to bring your data and write back to your data warehouse recurrently
- [URL Endpoint](https://docs.graphext.com/documentation/import-and-export/data-connections/url-endpoint.md): Fetch a specified endpoint of your choice to bring your data from anywhere
- [Enrichments and external providers](https://docs.graphext.com/documentation/import-and-export/data-enrichments.md): API Keys, APIFY, Phantombuster, Dataset examples, Kaggle, etc
- [Export your data](https://docs.graphext.com/documentation/import-and-export/export-data.md): Bring your data with you anywhere
- [Import data files](https://docs.graphext.com/documentation/import-and-export/import-files.md)
- [Update your data recurrently](https://docs.graphext.com/documentation/import-and-export/update-data.md): Setting up Graphext to update your data on a periodic basis
- [API Keys Connections](https://docs.graphext.com/documentation/integrations/api-keys/overview.md): Enrinch your data with AI, bring data from social media or use your favourite ML model
- [Airtable](https://docs.graphext.com/documentation/integrations/data-sources/airtable.md): Connect Graphext to Airtable and be able to bring your data and write back to your data warehouse recurrently
- [Amazon S3](https://docs.graphext.com/documentation/integrations/data-sources/amazon-s3.md): Connect Graphext to Amazon S3 and be able to bring your data and write back recurrently
- [Redshift](https://docs.graphext.com/documentation/integrations/data-sources/aws-redshift.md): Connect Graphext to Amazon Redshift and be able to bring your data and write back to your data warehouse recurrently
- [Azure Blob Storage](https://docs.graphext.com/documentation/integrations/data-sources/azure-blob.md): Connect Graphext to Azure Blob Storage and be able to bring your data and write back recurrently
- [Azure SQL](https://docs.graphext.com/documentation/integrations/data-sources/azuresql.md): Connect Graphext to a Azure SQL and be able to bring your data and write back recurrently
- [Databricks](https://docs.graphext.com/documentation/integrations/data-sources/databricks.md): Connect Graphext to Databricks and be able to bring your data and write back to your data warehouse recurrently
- [Google BigQuery](https://docs.graphext.com/documentation/integrations/data-sources/google-bigquery.md): Connect Graphext to Google BigQuery and be able to bring your data and write back to your data warehouse recurrently
- [Google Cloud Storage](https://docs.graphext.com/documentation/integrations/data-sources/google-cs.md): Connect Graphext to Google Cloud Storage and be able to bring your data and write back recurrently
- [Google Drive](https://docs.graphext.com/documentation/integrations/data-sources/google-drive.md): Connect Graphext to Google Drive and be able to bring your data and write back recurrently
- [Google Sheets](https://docs.graphext.com/documentation/integrations/data-sources/google-sheets.md): Connect Graphext to Google Sheets and be able to bring your data and write back recurrently
- [MySQL](https://docs.graphext.com/documentation/integrations/data-sources/mysql.md): Connect Graphext to a MySQL and be able to bring your data and write back recurrently
- [Notion](https://docs.graphext.com/documentation/integrations/data-sources/notion.md): Connect Graphext to Notion and be able to bring your data and write back to your data warehouse recurrently
- [OpenAI](https://docs.graphext.com/documentation/integrations/data-sources/open-ai.md): Enhance your projects with the latest OpenAI models
- [Import from Integrations](https://docs.graphext.com/documentation/integrations/data-sources/overview.md): Import and export from and to the most popular data sources
- [PostgreSQL](https://docs.graphext.com/documentation/integrations/data-sources/postgresql.md): Connect Graphext to a PostgreSQL and be able to bring your data and write back recurrently
- [Snowflake](https://docs.graphext.com/documentation/integrations/data-sources/snowflake.md): Connect Graphext to Snowflake and be able to bring your data and write back to your data warehouse recurrently
- [SQL Server](https://docs.graphext.com/documentation/integrations/data-sources/sqlserver.md): Connect Graphext to a SQL Server and be able to bring your data and write back recurrently
- [Tinybird](https://docs.graphext.com/documentation/integrations/data-sources/tinybird.md): Connect Graphext to Tinybird and be able to bring your data and write back to your data warehouse recurrently
- [URL Endpoint](https://docs.graphext.com/documentation/integrations/data-sources/url-endpoint.md): Fetch a specified endpoint of your choice to bring your data from anywhere
- [Clustering](https://docs.graphext.com/documentation/machine-learning/clustering.md): A modern visual advanced analytics platform for your business
- [Other analysis](https://docs.graphext.com/documentation/machine-learning/other-analysis.md): A modern visual advanced analytics platform for your business
- [Predictive models](https://docs.graphext.com/documentation/machine-learning/predictive-models.md): A modern visual advanced analytics platform for your business
- [Text analysis](https://docs.graphext.com/documentation/machine-learning/text-analysis.md): A modern visual advanced analytics platform for your business
- [Account Settings](https://docs.graphext.com/documentation/manage-workspace/account-settings.md): Manage your account preferences and much more
- [Manage connections](https://docs.graphext.com/documentation/manage-workspace/manage-connections.md): A modern visual advanced analytics platform for your business
- [Manage projects](https://docs.graphext.com/documentation/manage-workspace/manage-projects.md): Understand what projects are and how to handle them
- [Manage teams](https://docs.graphext.com/documentation/manage-workspace/manage-teams.md): Collaborate with your team mates and organize your projects in an efficient way
- [Export plots & charts](https://docs.graphext.com/documentation/share-present-publish/export-plots-and-chart.md): Get publish ready charts in minutes
- [Saving insights in a project](https://docs.graphext.com/documentation/share-present-publish/saving-insights.md): Take snapshots of your data for later inspection
- [Sharing an interactive project](https://docs.graphext.com/documentation/share-present-publish/share-an-interactive-projects.md): Package and share your analysis in an interactive environment
- [Data Sources](https://docs.graphext.com/faqs/data-sources.md): The FAQs about the supported integrations with data warehouses, APIs connections or supported type of files.
- [Features](https://docs.graphext.com/faqs/features.md): The FAQs about Graphext's features, types of analysis and limitations
- [Intro to Graphext](https://docs.graphext.com/faqs/intro-to-graphext.md): The FAQs about Graphext and your first steps into the platform
- [Performance](https://docs.graphext.com/faqs/performance.md): The FAQs about Graphext's engine, capacity and limits.
- [Pricing and Plans](https://docs.graphext.com/faqs/pricing-and-plans.md): The FAQs about Graphext pricing, free plan, enterprise options, etc.
- [Security & Privacy](https://docs.graphext.com/faqs/security-and-privacy.md): The FAQs about Graphext's privacy policies, GDPR, single-tenant options, etc
- [Support & Customer Service](https://docs.graphext.com/faqs/support.md): The FAQs about Graphext's customer support, professional services and trainnings
- [Scrape data with APIFY and analyze it with Graphext](https://docs.graphext.com/tutorials/data-extraction/apify-and-graphext.md): Start exploring your data and discovering insights in under 5 minutes
- [How To's and guides](https://docs.graphext.com/tutorials/how-tos-guides/overview.md): A set of tutorials to get you started using Graphext
- [Lead Scoring and Churn](https://docs.graphext.com/tutorials/lead-scoring-churn/overview.md): Learn the behaviour of your clients and maximize your revenue
- [Machine Learning Models](https://docs.graphext.com/tutorials/machine-learning/overview.md): Learn how to feed data to a model and interpret its results
- [Text Analysis](https://docs.graphext.com/tutorials/text-analysis/overview.md): Discover the power of free text analysis through industry standard methods
- [Tutorial Gallery](https://docs.graphext.com/tutorials/tutorials.md): End to end guides to help you get the maximum value from Graphext

## OpenAPI Specs

- [openapi](https://docs.graphext.com/api-reference/openapi.json)