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https://github.com/pdwaggoner/python-to-tidy-R
A Running List of Key Python Operations Translated to Tidy R
https://github.com/pdwaggoner/python-to-tidy-R
Last synced: 3 months ago
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A Running List of Key Python Operations Translated to Tidy R
- Host: GitHub
- URL: https://github.com/pdwaggoner/python-to-tidy-R
- Owner: pdwaggoner
- Created: 2023-09-11T15:53:03.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-08T15:17:26.000Z (6 months ago)
- Last Synced: 2024-05-08T16:55:10.908Z (6 months ago)
- Size: 154 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# From Python to Tidy R (and Back)
**A Running List of Key Python Operations Translated to (Mostly) Tidy R**
Frequently I am writing code in Python and R. And my team relies heavily on the [Tidyverse](https://www.tidyverse.org/) syntax. So, I am often translating key Python operations (pandas, matplotlib, etc.) to tidy R (dplyr, ggplot2, etc.). In an effort to ease that translation, and also to crowdsource a running directory of these translations, I created this repo.
This is just a start. **Please feel free to share and also directly contribute or revise via pulls or issues**.
*Note:* I recommend using the native pipe operator (`|>`) when constructing piped operations in practice, instead of the `magrittr` pipe (`%>%`). However, I used the latter in this repo because the `|` in the native R pipe threw off formatting of the markdown tables.
## Table of Contents
- [Key tasks](#Key-tasks)
- [Joining Data](#Joining-Data)
- [Iteration](#Iteration)
- [Iteration Over Lists](#Iteration-Over-Lists)
- [String Operations](#String-Operations)
- [Modeling and Machine Learning](#Modeling-and-Machine-Learning)
- [Network Modeling and Dynamics](#Network-Modeling-and-Dynamics)
- [Parallel Computing](https://github.com/pdwaggoner/python-to-tidy-R/blob/main/Parallel%20Computing.md)
----
## Key tasks
| Task / Operation | Python (Pandas) | Tidyverse (dplyr, ggplot2) |
|-------------------------|--------------------------------------|-----------------------------------|
| **Data Loading** | `import pandas as pd` | `library(readr)` |
| | `df = pd.read_csv('file.csv')` | `data <- read_csv('file.csv')` |
| **Select Columns** | `df[['col1', 'col2']]` | `data %>% select(col1, col2)` |
| **Filter Rows** | `df[df['col'] > 5]` | `data %>% filter(col > 5)` |
| **Arrange Rows** | `df.sort_values(by='col')` | `data %>% arrange(col)` |
| **Mutate (Add Columns)**| `df['new_col'] = df['col1'] + df['col2']` | `data %>% mutate(new_col = col1 + col2)` |
| **Group and Summarize** | `df.groupby('col').agg({'col2': 'mean'})` | `data %>% group_by(col) %>% summarize(mean_col2 = mean(col2))` |
| **Pivot/Wide to Long** | `pd.melt(df, id_vars=['id'], var_name='variable', value_name='value')` | `data %>% gather(variable, value, -id)` |
| **Long to Wide/Pivot** | `df.pivot(index='id', columns='variable', values='value')` | `data %>% spread(variable, value)` |
| **Data Visualization** | Matplotlib, Seaborn, Plotly, etc. | ggplot2 |
| | `import matplotlib.pyplot as plt` | `library(ggplot2)` |
| | `plt.scatter(df['x'], df['y'])` | `ggplot(data, aes(x=x, y=y)) + geom_point()` |
| **Data Reshaping** | `pd.concat([df1, df2], axis=0)` | `bind_rows(df1, df2)` |
| | `pd.concat([df1, df2], axis=1)` | `bind_cols(df1, df2)` |
| **String Manipulation** | `df['col'].str.replace('a', 'b')` | `data %>% mutate(col = str_replace(col, 'a', 'b'))` |
| **Date and Time** | `pd.to_datetime(df['date_col'])` | `data %>% mutate(date_col = as.Date(date_col))` |
| **Missing Data Handling**| `df.dropna()` | `data %>% drop_na()` |
| **Rename Columns** | `df.rename(columns={'old_col': 'new_col'})` | `data %>% rename(new_col = old_col)` |
| **Summary Statistics** | `df.describe()` | `data %>% summary()` or `data %>% glimpse()` |
## Joining Data
This is the only table that includes SQL given that most of the R/`dplyr` operations were patterned and named after many SQL operations.
| Join Type | SQL | Python (Pandas) | R (dplyr) |
|-----------------|------------------------------------------|----------------------------------------|----------------------------------------|
| **Inner Join** | `INNER JOIN` | `pd.merge(df1, df2, on='key')` | `inner_join(df1, df2, by='key')` |
| **Left Join** | `LEFT JOIN` | `pd.merge(df1, df2, on='key', how='left')` | `left_join(df1, df2, by='key')` |
| **Right Join** | `RIGHT JOIN` | `pd.merge(df1, df2, on='key', how='right')` | `right_join(df1, df2, by='key')` |
| **Full Outer Join** | `FULL OUTER JOIN` | `pd.merge(df1, df2, on='key', how='outer')` | `full_join(df1, df2, by='key')` |
| **Cross Join** | `CROSS JOIN` | `pd.merge(df1, df2, how='cross')` | Not directly supported, but can be achieved with `full_join` and filtering |
| **Anti Join** | Not directly supported | `pd.merge(df1, df2, on='key', how='left', indicator=True).query('_merge == "left_only"').drop('_merge', axis=1)` | Not directly supported, but can be achieved with `anti_join` function from dplyr or by using `filter()` and `!` condition |
| **Semi Join** | Not directly supported | `pd.merge(df1, df2, on='key', how='inner', indicator=True).query('_merge == "both"').drop('_merge', axis=1)` | Not directly supported, but can be achieved with `semi_join` function from dplyr or by using `filter()` and `!` condition |
| **Self Join** | `INNER JOIN` with the same table | `pd.merge(df, df, on='key')` | `inner_join(df, df, by='key')` |
| **Multiple Key Join** | `INNER JOIN` with multiple keys | `pd.merge(df1, df2, on=['key1', 'key2'])` | `inner_join(df1, df2, by=c('key1', 'key2'))` |
| **Join with Renamed Columns** | `INNER JOIN` with renamed columns | `pd.merge(df1.rename(columns={'col1': 'key'}), df2, on='key')` | `inner_join(rename(df1, key = col1), df2, by = 'key')` |
| **Join with Complex Condition** | `INNER JOIN` with complex conditions | `pd.merge(df1, df2, on='key', how='inner', left_on=(df1['col1'] > 10) & (df1['col2'] == df2['col3']))` | Not directly supported, but can be achieved with `filter()` and complex conditions |
| **Join with Different Key Names** | `INNER JOIN` with different key names | `pd.merge(df1, df2, left_on='key1', right_on='key2')` | `inner_join(df1, df2, by = c('key1' = 'key2'))` |
## Iteration
| Task / Operation | Python (Pandas) | Tidyverse (dplyr and purrr) |
|-----------------------------|--------------------------------------|-----------------------------------|
| **Iterate Over Rows** | `for index, row in df.iterrows():` | `data %>% rowwise() %>% mutate(new_col = your_function(col))` |
| | ` print(row['col1'], row['col2'])` | |
| **Map Function to Column** | `df['new_col'] = df['col'].apply(your_function)` | `data %>% mutate(new_col = map_dbl(col, your_function))` |
| **Apply Function to Column**| `df['new_col'] = your_function(df['col'])` | `data %>% mutate(new_col = your_function(col))` |
| **Group and Map** | `for group, group_df in df.groupby('group_col'):` | `data %>% group_by(group_col) %>% nest(data = .) %>% mutate(new_col = map(data, your_function))` |
| **Map Over List Column** | `df['new_col'] = df['list_col'].apply(lambda x: [your_function(i) for i in x])` | `data %>% mutate(new_col = map(list_col, ~map(your_function, .)))` |
| **Map with Anonymous Function** | - | `data %>% mutate(new_col = map_dbl(col, ~your_function(.)))` |
| **Map Multiple Columns** | `df['new_col'] = df.apply(lambda row: your_function(row['col1'], row['col2']), axis=1)` | `data %>% mutate(new_col = pmap_dbl(list(col1, col2), ~your_function(...)))` |
## Iteration Over Lists
| Task / Operation | Python (Pandas) | Tidyverse (dplyr and purrr) |
|-----------------------------------|-----------------------------------------|-------------------------------------------|
| **Map Function Across List Column**| `df['new_col'] = df['list_col'].apply(lambda x: [your_function(i) for i in x])` | `data %>% mutate(new_col = map(list_col, ~map(your_function, .)))` |
| **Nested Map in List Column** | `df['new_col'] = df['list_col'].apply(lambda x: [your_function(i) for i in x])` | `data %>% mutate(new_col = map(list_col, ~map(your_function, .)))` |
| **Nested Map Across Columns** | - | `data %>% mutate(new_col = map2(list(col1, col2), ~map(your_function, .)))` |
| **Nested Map Within List Column** | - | `data %>% mutate(new_col = map(list_col, ~map(your_function, .)))` |
| **Map Across Rows with Nested Map**| - | `data %>% mutate(new_col = pmap(list(col1, col2), ~list(your_function(.x), your_function(.y))))` |
| **Nested Map Within Nested List** | - | `data %>% mutate(new_col = map(list(list_col), ~map(your_function, .)))` |
| **Nested Map Across List of Lists** | `df['new_col'] = df['list_col'].apply(lambda x: [list(map(your_function, i)) for i in x])` | `data %>% mutate(new_col = map2(list(list_col1, list_col2), ~map2(your_function1, your_function2, .x, .y)))` |
| **Nested Map Across Rows and Lists**| - | `data %>% mutate(new_col = pmap(list(col1, col2, col3), ~list(your_function(.x), your_function(.y), your_function(.z))))` |
| **Map and Reduce Across List** | `df['new_col'] = df['list_col'].apply(lambda x: reduce(your_function, x))` | `data %>% mutate(new_col = map(list_col, ~reduce(your_function, .)))` |
| **Map and Reduce Across Rows** | `df['new_col'] = df.apply(lambda row: reduce(your_function, row[['col1', 'col2']]), axis=1)` | `data %>% mutate(new_col = pmap(list(col1, col2), ~reduce(your_function, .)))` |
## String Operations
| Task / Operation | Python (Pandas) | Tidyverse (dplyr and stringr) |
|--------------------------------|-----------------------------------|-----------------------------------------|
| **String Length** | `df['col'].str.len()` | `data %>% mutate(new_col = str_length(col))` |
| **Concatenate Strings** | `df['new_col'] = df['col1'] + df['col2']` | `data %>% mutate(new_col = str_c(col1, col2))` |
| **Split Strings** | `df['col'].str.split(', ')` | `data %>% mutate(new_col = str_split(col, ', '))` |
| **Substring** | `df['col'].str.slice(0, 5)` | `data %>% mutate(new_col = str_sub(col, 1, 5))` |
| **Replace Substring** | `df['col'].str.replace('old', 'new')` | `data %>% mutate(new_col = str_replace(col, 'old', 'new'))` |
| **Uppercase / Lowercase** | `df['col'].str.upper()` | `data %>% mutate(new_col = str_to_upper(col))` |
| | `df['col'].str.lower()` | `data %>% mutate(new_col = str_to_lower(col))` |
| **Strip Whitespace** | `df['col'].str.strip()` | `data %>% mutate(new_col = str_squish(col))` |
| **Check for Substring** | `df['col'].str.contains('pattern')` | `data %>% mutate(new_col = str_detect(col, 'pattern'))` |
| **Count Substring Occurrences** | `df['col'].str.count('pattern')` | `data %>% mutate(new_col = str_count(col, 'pattern'))` |
| **Find First Occurrence of Substring**| `df['col'].str.find('pattern')` | `data %>% mutate(new_col = str_locate(col, 'pattern')[, 1])` |
| **Extract Substring with Regex** | `df['col'].str.extract(r'(\d+)')` | `data %>% mutate(new_col = str_extract(col, '(\\d+)'))` |
| **Remove Duplicates in Strings** | - | `data %>% mutate(new_col = str_unique(col))` |
| **Pad Strings** | `df['col'].str.pad(width=10, side='right', fillchar='0')` | `data %>% mutate(new_col = str_pad(col, width = 10, side = 'right', pad = '0'))` |
| **Truncate Strings** | `df['col'].str.slice(0, 10)` | `data %>% mutate(new_col = str_sub(col, 1, 10))` |
| **Title Case** | - | `data %>% mutate(new_col = str_to_title(col))` |
| **Join List of Strings** | `'separator'.join(df['col'])` | `data %>% mutate(new_col = str_flatten(col, collapse = 'separator'))` |
| **Remove Punctuation** | - | `data %>% mutate(new_col = str_remove_all(col, '[[:punct:]]'))` |
| **String Encoding/Decoding** | - | `data %>% mutate(new_col = str_encode(col, to = 'UTF-8'))` |
## Modeling and Machine Learning
| Task / Operation | Python (scikit-learn) | R (various packages) |
|-------------------------------|----------------------------------------|----------------------------------------|
| **Data Preprocessing** | `from sklearn.preprocessing import ...` | `library(caret)` |
| | `from sklearn.pipeline import Pipeline` | `library(glmnet)` |
| | `preprocessor = ...` | `preprocess <- preProcess(data, ...)` |
| **Feature Scaling** | `StandardScaler()` | `preprocess$scaling` |
| **Feature Selection** | `SelectKBest()` | `caret::createFolds()` |
| **Data Splitting** | `train_test_split()` | `createDataPartition()` |
| **Model Initialization** | `model = ...()` | `model <- ...()` |
| **Model Training** | `model.fit(X_train, y_train)` | `model <- train(y ~ ., data = data)` |
| **Model Prediction** | `y_pred = model.predict(X_test)` | `y_pred <- predict(model, newdata)` |
| **Model Evaluation** | `accuracy_score(y_test, y_pred)` | `confusionMatrix(y_pred, y_true)` |
| **Hyperparameter Tuning** | `GridSearchCV()` | `tuneGrid(...)` |
| **Cross-Validation** | `cross_val_score()` | `trainControl(method = "cv")` |
| **Model Pipelining** | `pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])` | `model <- train(y ~ ., data = data, method = model, trControl = trainControl(method = "cv"))` |
| **Feature Engineering** | `from sklearn.preprocessing import ...` | `library(caret)` |
| | Custom feature transformers | Custom feature transformers |
| **Handling Missing Data** | `SimpleImputer()` | `preprocess$impute` |
| **Encoding Categorical Data** | `OneHotEncoder()` | `dummyVars()` |
| **Dimensionality Reduction** | `PCA()` | `preprocess$reduce` |
| **Model Selection** | `GridSearchCV()` | `caret::train()` |
| **Ensemble Learning** | Various ensemble methods | `caret::train()` with `method="stack"` |
| **Regularization** | Lasso, Ridge, Elastic Net, etc. | `glmnet()` |
| **Model Interpretability** | SHAP, Lime, etc. | DALEX, iml, etc. |
| **Model Export/Serialization** | `joblib` or `pickle` | `saveRDS` or other formats |
| **Deploying Models** | Web frameworks (e.g., Flask, Django) | Web frameworks (e.g., Shiny, Plumber) |
| **Batch Scoring** | Scripting or automation tools | R batch processing |
| **Feature Scaling/Normalization**| `StandardScaler()`, `MinMaxScaler()`, etc. | `scale()`, `normalize()`, etc. |
| **Feature Selection with L1 Regularization** | `SelectFromModel()`, `Lasso()` | `glmnet()`, `cv.glmnet()` |
| **Handling Imbalanced Data** | `RandomUnderSampler()`, `SMOTE()`, etc. | `caret::train()` with `weights` or `sampling` |
| **Model Evaluation Metrics** | `classification_report()`, `confusion_matrix()`, `mean_squared_error()`, etc. | `confusionMatrix()`, `postResample()`, `RMSE`, etc. |
| **Feature Importance** | `.feature_importances_` (Random Forest, etc.) | `varImp()`, `vip()`, etc. |
| **Model Persistence** | `joblib`, `pickle`, `sklearn.externals` | `saveRDS`, `save()`, `serialize()`, etc. |
| **Time Series Forecasting** | `Prophet`, `ARIMA`, `ExponentialSmoothing`, etc. | `forecast`, `prophet`, `auto.arima`, etc. |
| **Natural Language Processing (NLP)** | `nltk`, `spaCy`, `textblob`, etc. | `tm`, `quanteda`, `udpipe`, `tm.plugin.webmining`, etc. |
| **Deep Learning** | `Keras`, `TensorFlow`, `PyTorch`, etc. | `keras`, `tensorflow`, `torch`, `mxnet`, etc. |
| **Model Interpretation** | `SHAP`, `LIME`, `ELI5`, etc. | `DALEX`, `iml`, `iBreakDown`, `lime`, etc. |
| **Model Deployment in Production** | Containers, cloud platforms (e.g., Docker, Kubernetes, AWS SageMaker) | Containers, Shiny, Plumber, APIs, cloud platforms |
## Network Modeling and Dynamics
| Task / Operation | Python (NetworkX) | R (various packages) |
|---------------------------------|--------------------------------------|----------------------------------------|
| **Network Creation** | `G = nx.Graph()`, `G.add_node()`, `G.add_edge()` | `igraph::graph()`, `add_vertices()`, `add_edges()` |
| **Node and Edge Attributes** | `G.nodes[node]['attribute'] = value`, `G.edges[edge]['attribute'] = value` | `V(graph)$attribute <- value`, `E(graph)$attribute <- value` |
| **Network Visualization** | `nx.draw(G)`, `matplotlib` for customization | `plot(graph)`, `igraph`, `ggplot2`, `visNetwork`, etc. |
| **Network Measures** | `nx.degree_centrality(G)`, `nx.betweenness_centrality(G)`, `nx.clustering(G)`, etc. | `degree()`, `betweenness()`, `transitivity()`, etc. |
| **Community Detection** | `community.detect()` (e.g., Louvain, Girvan-Newman) | `cluster_walktrap()`, `cluster_fast_greedy()`, `cluster_leading_eigen()`, etc. |
| **Link Prediction** | `link_prediction.method()` (e.g., Common Neighbors, Jaccard Coefficient) | `link_prediction.method()` (e.g., Adamic-Adar, Preferential Attachment) |
| **Network Filtering/Selection** | `G.subgraph(nodes)` | `subgraph(graph, vertices)` |
| **Network Embedding** | `node2vec`, `GraphSAGE`, etc. | `walktrap.community`, `fastgreedy.community`, etc. |
| **Network Simulation** | `nx.erdos_renyi_graph()`, `nx.watts_strogatz_graph()`, etc. | `igraph::erdos.renyi.game()`, `igraph::watts.strogatz.game()`, etc. |
| **Network Analysis Pipelines** | Custom pipelines using NetworkX, Pandas, and other libraries | Custom pipelines using igraph, dplyr, and other packages |
| **Dynamic Network Analysis** | `dynetx` for dynamic networks | `tsna` for temporal networks, `dyngraph` for dynamic graphs, etc. |
| **Geospatial Network Analysis** | `osmnx` for urban network analysis | `stplanr` for transport planning, `spatnet` for spatial network analysis, etc. |
| **Network Modeling for Machine Learning** | Integration with scikit-learn, PyTorch, etc. | Integration with caret, glmnet, keras, etc. |
| **Community Visualization** | Visualization of detected communities using network layouts | `igraph::plot.igraph()` with community coloring |
| **Path Analysis** | Shortest paths, k-shortest paths, and all simple paths | `get.shortest.paths()`, `all.simple.paths()` |
| **Centrality Analysis** | Closeness centrality, eigenvector centrality, Katz centrality, etc. | `closeness()`, `eigen_centrality()`, `katz_centrality()`, etc. |
| **Structural Role Analysis** | Structural equivalence, equivalence-based roles | `structural_equivalence()`, `role_equiv()`, etc. |
| **Network Robustness Analysis** | Network attack simulations, robustness metrics | `robustness()` function, `remove_vertices()`, etc. |
| **Temporal Network Analysis** | Temporal networks, evolving networks | `dynnet` package for dynamic networks, temporal extensions of `igraph` functions |
| **Multiplex Network Analysis** | Analyzing multiple layers of networks | `multiplex` package for multilayer networks, `mgm` package for multilayer graphical models |
| **Network Alignment** | Aligning nodes in two or more networks | `netAlign` package for network alignment, `gmatch` package for graph matching |
| **Dynamic Community Detection** | Detecting evolving communities over time | `dynCOMM` for dynamic community detection |
| **Network Generative Models** | Generating networks from various models (e.g., ER, BA, etc.) | `igraph::sample_gnm()`, `igraph::sample_degseq()`, etc. |
| **Geospatial Network Analysis** | Geospatial network analysis and routing | `stplanr` for transport planning, `spatnet` for spatial network analysis, etc. |
| **Network Modeling for Machine Learning** | Integrating network data with machine learning libraries | Combining `igraph` or custom network features with caret, glmnet, keras, etc. |