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Python to R translation of 30 essential Pandas methods introduced by Avi Chawla in The Only 30 Methods You Should Master To Become A Pandas Pro published on TowardsDataScience.
# enable python in RMarkdown
library(reticulate)Load R’s build-in dataframe airquality, which contains
some missing and save it in the data subfolder under the project folder.
Here we used write.csv() in base R rather than
write_csv() from the readr package in
tidyverse. However, the latter is twice as fast as the former so
consider use write_csv() for larger datasets.
df = airquality
df |> head() # we can see that the dataset contains some missing values Ozone Solar.R Wind Temp Month Day
1 41 190 7.4 67 5 1
2 36 118 8.0 72 5 2
3 12 149 12.6 74 5 3
4 18 313 11.5 62 5 4
5 NA NA 14.3 56 5 5
6 28 NA 14.9 66 5 6#write the dataframe
write.csv(df, 'data/airquality.csv')Read the csv file into the Python environment.
import pandas as pd
df = pd.read_csv('data/airquality.csv')
df.head(5) Unnamed: 0 Ozone Solar.R Wind Temp Month Day
0 1 41.0 190.0 7.4 67 5 1
1 2 36.0 118.0 8.0 72 5 2
2 3 12.0 149.0 12.6 74 5 3
3 4 18.0 313.0 11.5 62 5 4
4 5 NaN NaN 14.3 56 5 5info() method can be used to print the missing-value
stats and the datatypes. Recall that we can also get datatypes info
using .dtypes.
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 153 entries, 0 to 152
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 153 non-null int64
1 Ozone 116 non-null float64
2 Solar.R 146 non-null float64
3 Wind 153 non-null float64
4 Temp 153 non-null int64
5 Month 153 non-null int64
6 Day 153 non-null int64
dtypes: float64(3), int64(4)
memory usage: 8.5 KBsummary() in base R returns the number of missings as
well as some summary statistics. Note that it only flags missing info
for columns with any missings. To get dataype info, recall we can use
the glimpse() or summary() function. There is
also a package naniar in R that specialize in identifying,
visualizing and handling missing values, I provided some
examples here in case you are interested.
df |> summary() Ozone Solar.R Wind Temp
Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
NA's :37 NA's :7
Month Day
Min. :5.000 Min. : 1.0
1st Qu.:6.000 1st Qu.: 8.0
Median :7.000 Median :16.0
Mean :6.993 Mean :15.8
3rd Qu.:8.000 3rd Qu.:23.0
Max. :9.000 Max. :31.0
sessionInfo()R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
[3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Australia.utf8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] reticulate_1.30
loaded via a namespace (and not attached):
[1] Rcpp_1.0.11 pillar_1.9.0 compiler_4.2.1 bslib_0.5.0
[5] later_1.3.1 jquerylib_0.1.4 git2r_0.32.0 workflowr_1.7.0
[9] tools_4.2.1 digest_0.6.33 lattice_0.20-45 jsonlite_1.8.7
[13] evaluate_0.21 lifecycle_1.0.3 tibble_3.2.1 png_0.1-8
[17] pkgconfig_2.0.3 rlang_1.1.1 Matrix_1.4-1 cli_3.6.1
[21] rstudioapi_0.15.0 yaml_2.3.7 xfun_0.39 fastmap_1.1.1
[25] withr_2.5.0 stringr_1.5.0 knitr_1.43 fs_1.6.2
[29] vctrs_0.6.3 sass_0.4.7 grid_4.2.1 rprojroot_2.0.3
[33] here_1.0.1 glue_1.6.2 R6_2.5.1 fansi_1.0.4
[37] rmarkdown_2.23 magrittr_2.0.3 whisker_0.4.1 promises_1.2.0.1
[41] htmltools_0.5.5 renv_1.0.0 httpuv_1.6.11 utf8_1.2.3
[45] stringi_1.7.12 cachem_1.0.8