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rPandas is an R package designed to serve as a translation layer, allowing R users to leverage the power and speed of the Python pandas library without ever leaving their R session.
For R users familiar with dplyr or data.table, the rPandas syntax will feel natural. The package provides a set of R functions (e.g., rp_filter(), rp_select()) that:
Capture R code (like carat > 1).
Translate it into an equivalent Python/pandas command string.
Execute the Python code in the background via the reticulate package.
Return the resulting data.frame back to your R session.
This allows you to write R code while the data processing is handled by Python’s pandas library.
rPandas depends on a working Python installation and the pandas library. The reticulate package handles the R‑to‑Python connection.
You can install pandas into a dedicated R environment by running:
If you encounter issues (e.g., “pandas not found”), rPandas provides a built‑in health check:
The health check prints the Python path reticulate is using and
whether pandas is installed.
You can list all available conda environments (if you use Conda) with:
Once you identify the desired environment (e.g., the first one), you can tell reticulate to use it before loading rPandas:
# Replace with the path from conda_list()
reticulate::use_python(python = reticulate::conda_list()$python[1], required = TRUE)
# Or, if you prefer to use a conda environment by name:
reticulate::use_condaenv("your_environment_name", required = TRUE)For system Python installations, simply provide the path to the Python binary:
After setting the environment, rerun rp_check_env() to
confirm everything is ready. For more detailed guidance, see the reticulate
documentation.
Note: The code chunks above are not executed when building the vignette (they are meant to be run interactively by the user).
All examples will use the diamonds dataset from the ggplot2 package.
# Make sure ggplot2 is installed to access the data
data(diamonds, package = "ggplot2")
head(diamonds)
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.481. rp_filter(): This verb filters rows based on a logical expression, similar to dplyr::filter(). It supports common logical operators: & (AND), | (OR), ! (NOT), %in%, and %notin%
# Simple condition
v1 <- rp_filter(diamonds, carat > 1)
print(head(v1))
#> carat cut color clarity depth table price x y z
#> 1 1.17 Very Good J I1 60.2 61 2774 6.83 6.90 4.13
#> 2 1.01 Premium F I1 61.8 60 2781 6.39 6.36 3.94
#> 3 1.01 Fair E I1 64.5 58 2788 6.29 6.21 4.03
#> 4 1.01 Premium H SI2 62.7 59 2788 6.31 6.22 3.93
#> 5 1.05 Very Good J SI2 63.2 56 2789 6.49 6.45 4.09
#> 6 1.05 Fair J SI2 65.8 59 2789 6.41 6.27 4.18
# AND: multiple conditions
v2 <- rp_filter(diamonds, carat > 1 & cut == "Ideal")
print(head(v2))
#> carat cut color clarity depth table price x y z
#> 1 1.01 Ideal I I1 61.5 57 2844 6.45 6.46 3.97
#> 2 1.02 Ideal H SI2 61.6 55 2856 6.49 6.43 3.98
#> 3 1.02 Ideal I I1 61.7 56 2872 6.44 6.49 3.99
#> 4 1.02 Ideal J SI2 60.3 54 2879 6.53 6.50 3.93
#> 5 1.01 Ideal I I1 61.5 57 2896 6.46 6.45 3.97
#> 6 1.02 Ideal I I1 61.7 56 2925 6.49 6.44 3.99
# OR: use | (pipe)
v3 <- rp_filter(diamonds, color == "D" | color == "E")
print(head(v3))
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
#> 5 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
#> 6 0.32 Premium E I1 60.9 58 345 4.38 4.42 2.68
# NOT: use !
v4 <- rp_filter(diamonds, !(price > 10000))
print(head(v4))
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# %in% operator
v5 <- rp_filter(diamonds, color %in% c("D", "E", "F"))
print(head(v5))
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
#> 5 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
#> 6 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
# %notin% (if implemented)
v6 <- rp_filter(diamonds, color %notin% c("D", "E", "F"))
print(head(v6))
#> carat cut color clarity depth table price x y z
#> 1 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 2 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 3 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#> 4 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
#> 5 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
#> 6 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.392. rp_select(): This verb selects specific columns by name, similar to dplyr::select().
# Select three columns
v4 <- rp_select(diamonds, carat, cut, price)
print(head(v4))
#> carat cut price
#> 1 0.23 Ideal 326
#> 2 0.21 Premium 326
#> 3 0.23 Good 327
#> 4 0.29 Premium 334
#> 5 0.31 Good 335
#> 6 0.24 Very Good 3363. rp_sort(): This verb sorts the data frame by one or more columns, similar to dplyr::arrange().
# Sort by price (ascending by default)
v8 <- rp_sort(diamonds, price)
print(head(v8))
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
# Use desc() to sort in descending order
v9 <- rp_sort(diamonds, desc(price))
print(head(v9))
#> carat cut color clarity depth table price x y z
#> 1 2.29 Premium I VS2 60.8 60 18823 8.50 8.47 5.16
#> 2 2.00 Very Good G SI1 63.5 56 18818 7.90 7.97 5.04
#> 3 1.51 Ideal G IF 61.7 55 18806 7.37 7.41 4.56
#> 4 2.07 Ideal G SI2 62.5 55 18804 8.20 8.13 5.11
#> 5 2.00 Very Good H SI1 62.8 57 18803 7.95 8.00 5.01
#> 6 2.29 Premium I SI1 61.8 59 18797 8.52 8.45 5.24
# Sort by multiple columns
v10 <- rp_sort(diamonds, cut, desc(price))
print(head(v10))
#> carat cut color clarity depth table price x y z
#> 1 2.01 Fair G SI1 70.6 64 18574 7.43 6.64 4.69
#> 2 2.02 Fair H VS2 64.5 57 18565 8.00 7.95 5.14
#> 3 4.50 Fair J I1 65.8 58 18531 10.23 10.16 6.72
#> 4 2.00 Fair G VS2 67.6 58 18515 7.65 7.61 5.16
#> 5 2.51 Fair H SI2 64.7 57 18308 8.44 8.50 5.48
#> 6 3.01 Fair I SI2 65.8 56 18242 8.99 8.94 5.904. rp_mutate(): This verb creates new columns or modifies existing ones, similar to dplyr::mutate(). You can also remove columns using the to_remove argument.
# Create a new column
v11 <- rp_mutate(diamonds, price_per_carat = price / carat)
print(head(v11))
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#> price_per_carat
#> 1 1417.391
#> 2 1552.381
#> 3 1421.739
#> 4 1151.724
#> 5 1080.645
#> 6 1400.000
# Create multiple columns
v12 <- rp_mutate(
diamonds,
price_per_carat = price / carat,
depth_pct = depth / 100
)
print(head(v12))
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#> price_per_carat depth_pct
#> 1 1417.391 0.615
#> 2 1552.381 0.598
#> 3 1421.739 0.569
#> 4 1151.724 0.624
#> 5 1080.645 0.633
#> 6 1400.000 0.628
# Remove one or more columns
v13 <- rp_mutate(diamonds, to_remove = c("table", "depth"))
print(head(v13))
#> carat cut color clarity price x y z
#> 1 0.23 Ideal E SI2 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 336 3.94 3.96 2.485. rp_summarize(): This verb collapses a data frame into a summary, often after grouping. It is similar to dplyr::summarise().
# Summarize the entire data frame
v14 <- rp_summarize(diamonds, avg_price = mean(price), max_carat = max(carat))
print(v14)
#> price carat
#> 1 3932.8 NaN
#> 2 NaN 5.01
# Group by one column (unquoted)
v15 <- rp_summarize(diamonds, avg_price = mean(price), .by = cut)
print(v15)
#> cut avg_price
#> 1 Fair 4358.758
#> 2 Good 3928.864
#> 3 Very Good 3981.760
#> 4 Premium 4584.258
#> 5 Ideal 3457.542
# Group by multiple columns (unquoted)
v16 <- rp_summarize(
diamonds,
avg_price = mean(price),
count = n(),
.by = c(cut, color)
)
print(head(v16))
#> cut color avg_price count
#> 1 Fair D 4291.061 163
#> 2 Fair E 3682.312 224
#> 3 Fair F 3827.003 312
#> 4 Fair G 4239.255 314
#> 5 Fair H 5135.683 303
#> 6 Fair I 4685.446 175
# Grouping also accepts quoted column names
v17 <- rp_summarize(
diamonds,
avg_price = mean(price),
.by = c("cut", "color")
)
print(head(v17))
#> cut color avg_price
#> 1 Fair D 4291.061
#> 2 Fair E 3682.312
#> 3 Fair F 3827.003
#> 4 Fair G 4239.255
#> 5 Fair H 5135.683
#> 6 Fair I 4685.4466. rp_calculate()– Apply Multiple Functions to Multiple Columns This verb is a powerful alternative to rp_summarize. It applies the same set of functions (e.g., “mean”, “sd”) to every selected column.
# Apply two functions to two columns, grouped by 'cut'
v13 <- rp_calculate(
diamonds,
price, carat,
the.functions = c("mean", "sd"),
.by = cut
)
print(head(v13))
#> cut price.mean price.std carat.mean carat.std
#> 1 Fair 4358.758 3560.387 1.0461366 0.5164043
#> 2 Good 3928.864 3681.590 0.8491847 0.4540544
#> 3 Very Good 3981.760 3935.862 0.8063814 0.4594354
#> 4 Premium 4584.258 4349.205 0.8919549 0.5152616
#> 5 Ideal 3457.542 3808.401 0.7028370 0.43287637. rp_first_k_rows() and rp_last_k_rows() – First/Last K Rows per Group These verbs extract the first or last k rows from the whole data frame, or from each group when .by is provided.
# First 3 rows overall
v19 <- rp_first_k_rows(diamonds, k = 3)
print(v19)
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# Last 2 rows per group (cut and clarity)
v20 <- rp_last_k_rows(diamonds, k = 2, .by = c(cut, clarity))
print(head(v20))
#> carat cut color clarity depth table price x y z
#> 1 0.70 Fair J VVS1 67.6 54 1691 5.56 5.41 3.71
#> 2 0.50 Fair D VVS1 65.9 64 1792 4.92 5.03 3.28
#> 3 0.52 Fair F IF 64.6 58 2144 5.04 5.17 3.30
#> 4 0.47 Fair D IF 60.6 60 2211 5.09 4.98 3.05
#> 5 0.55 Good F IF 60.8 60 2266 5.26 5.36 3.23
#> 6 0.54 Premium F IF 61.9 60 2391 5.26 5.21 3.24
# Both quoted and unquoted group specifications work
v21 <- rp_first_k_rows(diamonds, k = 1, .by = c("cut", "color"))
print(v21)
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48
#> 7 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47
#> 8 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53
#> 9 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49
#> 10 0.23 Ideal J VS1 62.8 56.0 340 3.93 3.90 2.46
#> 11 0.22 Premium F SI1 60.4 61.0 342 3.88 3.84 2.33
#> 12 0.30 Ideal I SI2 62.0 54.0 348 4.31 4.34 2.68
#> 13 0.30 Good I SI2 63.3 56.0 351 4.26 4.30 2.71
#> 14 0.23 Very Good E VS2 63.8 55.0 352 3.85 3.92 2.48
#> 15 0.23 Very Good G VVS2 60.4 58.0 354 3.97 4.01 2.41
#> 16 0.23 Very Good D VS2 60.5 61.0 357 3.96 3.97 2.40
#> 17 0.23 Very Good F VS1 60.9 57.0 357 3.96 3.99 2.42
#> 18 0.23 Good F VS1 58.2 59.0 402 4.06 4.08 2.37
#> 19 0.31 Good H SI1 64.0 54.0 402 4.29 4.31 2.75
#> 20 0.26 Good D VS2 65.2 56.0 403 3.99 4.02 2.61
#> 21 0.23 Ideal G VS1 61.9 54.0 404 3.93 3.95 2.44
#> 22 0.22 Premium D VS2 59.3 62.0 404 3.91 3.88 2.31
#> 23 0.30 Premium J SI2 59.3 61.0 405 4.43 4.38 2.61
#> 24 0.30 Ideal D SI1 62.5 57.0 552 4.29 4.32 2.69
#> 25 0.31 Premium G SI1 61.8 58.0 553 4.35 4.32 2.68
#> 26 0.30 Premium H SI1 62.9 59.0 554 4.28 4.24 2.68
#> 27 0.96 Fair F SI2 66.3 62.0 2759 6.27 5.95 4.07
#> 28 0.81 Ideal F SI2 58.8 57.0 2761 6.14 6.11 3.60
#> 29 0.91 Fair H SI2 64.4 57.0 2763 6.11 6.09 3.93
#> 30 0.77 Ideal H VS2 62.0 56.0 2763 5.89 5.86 3.64
#> 31 0.72 Good G VS2 59.7 60.5 2776 5.80 5.84 3.47
#> 32 0.84 Fair G SI1 55.1 67.0 2782 6.39 6.20 3.47
#> 33 1.05 Fair J SI2 65.8 59.0 2789 6.41 6.27 4.18
#> 34 0.90 Fair I SI1 67.3 59.0 2804 5.93 5.84 3.96
#> 35 0.75 Fair D SI2 64.6 57.0 2848 5.74 5.72 3.708. rp_count() – Count Rows (Overall or by Group) This verb returns the number of rows in the data frame, optionally by groups.
# Total row count
v22 <- rp_count(diamonds)
print(v22)
#> n
#> 1 53940
# Count per group
v23 <- rp_count(diamonds, .by = cut)
print(v23)
#> cut n
#> 1 Fair 1610
#> 2 Good 4906
#> 3 Very Good 12082
#> 4 Premium 13791
#> 5 Ideal 21551
# Count per combination of multiple groups
v24 <- rp_count(diamonds, .by = c(cut, color))
print(head(v24))
#> cut color n
#> 1 Fair D 163
#> 2 Fair E 224
#> 3 Fair F 312
#> 4 Fair G 314
#> 5 Fair H 303
#> 6 Fair I 175All rPandas functions are “pipe-friendly” and use .data as their first argument, allowing you to chain operations together using magrittr’s %>% pipe (or the native R |>).
# Load the pipe
v25 <- diamonds |>
rp_filter(carat > 1 & color == "D") |>
rp_mutate(price_per_carat = price / carat) |>
rp_summarize(avg_ppc = mean(price_per_carat), .by = cut) |>
rp_sort(desc(avg_ppc))
print(head(v25))
#> cut avg_ppc
#> 1 Ideal 7546.163
#> 2 Very Good 6789.316
#> 3 Premium 6548.397
#> 4 Good 5784.918
#> 5 Fair 5414.87A key feature of rPandas is its role as a learning and translation tool. Every user-facing verb has a return.as argument that lets you inspect the Python code it generates.
You can set return.as to:
“result” (default): Returns the final R data frame.
“code”: Returns the generated Python command as a string.
“all”: Returns a list containing both the result and the code.
# See the code for a simple filter
rp_filter(diamonds, carat > 1 & price < 400, return.as = "code")
#> [1] "df.query('(carat > 1) and (price < 400)')"
# See the code for a mutate
rp_mutate(diamonds, ppc = price / carat, return.as = "code")
#> [1] "df.assign(ppc = lambda x: (x['price'] / x['carat']))"
# See the code for a complex summary
rp_summarize(
diamonds,
avg_price = mean(price),
count = n(),
.by = c(cut, color),
return.as = "code"
)
#> [1] "df.groupby(['cut', 'color'], as_index=False, observed=True).agg(avg_price = ('price', 'mean'), count = ('price', 'size'))"By default, when you request return.as = “code”, the generated Python code uses the placeholder rpandas_df_in as the DataFrame name. This is because the function only receives the data object, not its name. However, you can provide a custom name using the table_name argument (available in all verbs). This is especially useful when you want to copy‑paste the code into a Python script or notebook.
# Default placeholder
rp_filter(diamonds, carat > 1, return.as = "code")
#> [1] "df.query('carat > 1')"
# With custom table name
rp_filter(diamonds, carat > 1, table_name = "diamonds", return.as = "code")
#> [1] "diamonds.query('carat > 1')"The output changes from: “rpandas_df_in.query(‘(carat > 1)’)” to: “diamonds.query(‘(carat > 1)’)”. This works for all verbs – rp_select, rp_mutate, rp_summarize, etc. Simply pass table_name = “your_data_frame_name” as an argument.
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