@@ -116,90 +116,90 @@ Table below describes some data formats:
116116 | storage/sharing:
117117
118118 * - :ref: `Pickle <pickle >`
119- - π΄
119+ - β
120120 - π‘
121- - π’
121+ - β
122122 - π‘
123123 - π‘
124- - π΄
124+ - β
125125
126126 * - :ref: `CSV <csv >`
127- - π’
128- - π΄
129- - π΄
130- - π’
127+ - β
128+ - β
129+ - β
130+ - β
131131 - π‘
132- - π’
132+ - β
133133
134134 * - :ref: `Feather <feather >`
135- - π΄
136- - π’
137- - π΄
138- - π’
139- - π΄
140- - π΄
135+ - β
136+ - β
137+ - β
138+ - β
139+ - β
140+ - β
141141
142142 * - :ref: `Parquet <parquet >`
143- - π΄
144- - π’
143+ - β
144+ - β
145145 - π‘
146- - π’
146+ - β
147147 - π‘
148- - π’
148+ - β
149149
150150 * - :ref: `npy <npy >`
151- - π΄
151+ - β
152152 - π‘
153- - π΄
154- - π΄
155- - π’
156- - π΄
153+ - β
154+ - β
155+ - β
156+ - β
157157
158158 * - :ref: `HDF5 <hdf5 >`
159- - π΄
160- - π’
161- - π΄
162- - π΄
163- - π’
164- - π’
159+ - β
160+ - β
161+ - β
162+ - β
163+ - β
164+ - β
165165
166166 * - :ref: `NetCDF4 <netcdf4 >`
167- - π΄
168- - π’
169- - π΄
170- - π΄
171- - π’
172- - π’
167+ - β
168+ - β
169+ - β
170+ - β
171+ - β
172+ - β
173173
174174 * - :ref: `JSON <json >`
175- - π’
176- - π΄
175+ - β
176+ - β
177177 - π‘
178- - π΄
179- - π΄
180- - π’
178+ - β
179+ - β
180+ - β
181181
182182 * - :ref: `Excel <excel >`
183- - π΄
184- - π΄
185- - π΄
183+ - β
184+ - β
185+ - β
186186 - π‘
187- - π΄
188- - π’
187+ - β
188+ - β
189189
190190 * - :ref: `Graph formats <graph >`
191191 - π‘
192192 - π‘
193- - π΄
194- - π΄
195- - π΄
193+ - β
194+ - β
195+ - β
196196 - π‘
197197
198198.. important ::
199199
200- - π’ : Good
200+ - β
: Good
201201 - π‘ : Ok / depends on a case
202- - π΄ : Bad
202+ - β : Bad
203203
204204
205205Storing arbitrary Python objects
@@ -216,10 +216,10 @@ Pickle
216216 - **Type **: Binary format
217217 - **Packages needed: ** None (:mod: `pickle `-module is included with Python).
218218 - **Space efficiency: ** π‘
219- - **Arbitrary data: ** π’
219+ - **Arbitrary data: ** β
220220 - **Tidy data: ** π‘
221221 - **Array data: ** π‘
222- - **Long term archival/sharing: ** π΄ ! See warning below.
222+ - **Long term archival/sharing: ** β ! See warning below.
223223 - **Best use cases: ** Saving Python objects for debugging.
224224
225225.. warning ::
@@ -282,11 +282,11 @@ CSV (comma-separated values)
282282
283283 - **Type: ** Text format
284284 - **Packages needed: ** numpy, pandas
285- - **Space efficiency: ** π΄
286- - **Arbitrary data: ** π΄
287- - **Tidy data: ** π’
285+ - **Space efficiency: ** β
286+ - **Arbitrary data: ** β
287+ - **Tidy data: ** β
288288 - **Array data: ** π‘
289- - **Long term archival/sharing: ** π’
289+ - **Long term archival/sharing: ** β
290290 - **Best use cases: ** Sharing data. Small data. Data that needs to be human-readable.
291291
292292CSV is by far the most popular file format, as it is human-readable and easily shareable.
@@ -367,11 +367,11 @@ Feather
367367
368368 - **Type: ** Binary format
369369 - **Packages needed: ** pandas, pyarrow
370- - **Space efficiency: ** π’
371- - **Arbitrary data: ** π΄
372- - **Tidy data: ** π’
373- - **Array data: ** π΄
374- - **Long term archival/sharing: ** π΄
370+ - **Space efficiency: ** β
371+ - **Arbitrary data: ** β
372+ - **Tidy data: ** β
373+ - **Array data: ** β
374+ - **Long term archival/sharing: ** β
375375 - **Best use cases: ** Temporary storage of tidy data.
376376
377377`Feather <https://arrow.apache.org/docs/python/feather.html >`__ is a file format for storing data frames quickly.
@@ -408,11 +408,11 @@ Parquet
408408
409409 - **Type: ** Binary format
410410 - **Packages needed: ** pandas, pyarrow
411- - **Space efficiency: ** π’
411+ - **Space efficiency: ** β
412412 - **Arbitrary data: ** π‘
413- - **Tidy data: ** π’
413+ - **Tidy data: ** β
414414 - **Array data: ** π‘
415- - **Long term archival/sharing: ** π’
415+ - **Long term archival/sharing: ** β
416416 - **Best use cases: ** Working with big datasets in tidy data format. Archival of said data.
417417
418418`Parquet <https://arrow.apache.org/docs/python/parquet.html >`__ is a standardized open-source
@@ -495,10 +495,10 @@ npy (numpy array format)
495495 - **Type **: Binary format
496496 - **Packages needed: ** numpy
497497 - **Space efficiency: ** π‘
498- - **Arbitrary data: ** π’
499- - **Tidy data: ** π΄
500- - **Array data: ** π’
501- - **Long term archival/sharing: ** π΄
498+ - **Arbitrary data: ** β
499+ - **Tidy data: ** β
500+ - **Array data: ** β
501+ - **Long term archival/sharing: ** β
502502 - **Best use cases: ** Saving numpy arrays temporarily.
503503
504504If you want to temporarily store numpy arrays, you can use the :func: `numpy.save `- and :func: `numpy.load `-functions::
@@ -532,11 +532,11 @@ HDF5 (Hierarchical Data Format version 5)
532532
533533 - **Type: ** Binary format
534534 - **Packages needed: ** numpy, pandas, PyTables, h5py
535- - **Space efficiency: ** π’
536- - **Arbitrary data: ** π΄
537- - **Tidy data: ** π΄
538- - **Array data: ** π’
539- - **Long term archival/sharing: ** π’
535+ - **Space efficiency: ** β
536+ - **Arbitrary data: ** β
537+ - **Tidy data: ** β
538+ - **Array data: ** β
539+ - **Long term archival/sharing: ** β
540540 - **Best use cases: ** Working with big datasets in array data format.
541541
542542HDF5 is a high performance storage format for storing large amounts of data in multiple datasets in a single file.
@@ -601,11 +601,11 @@ NetCDF4 (Network Common Data Form version 4)
601601
602602 - **Type **: Binary format
603603 - **Packages needed: ** pandas, netCDF4/h5netcdf, xarray
604- - **Space efficiency: ** π’
605- - **Arbitrary data: ** π΄
606- - **Tidy data: ** π΄
607- - **Array data: ** π’
608- - **Long term archival/sharing: ** π’
604+ - **Space efficiency: ** β
605+ - **Arbitrary data: ** β
606+ - **Tidy data: ** β
607+ - **Array data: ** β
608+ - **Long term archival/sharing: ** β
609609 - **Best use cases: ** Working with big datasets in array data format. Especially useful if the dataset contains spatial or temporal dimensions. Archiving or sharing those datasets.
610610
611611NetCDF4 is a data format that uses HDF5 as its file format, but it has standardized structure of datasets and metadata related to these datasets.
@@ -679,11 +679,11 @@ JSON (JavaScript Object Notation)
679679
680680 - **Type **: Text format
681681 - **Packages needed: ** None (:mod: `json `-module is included with Python).
682- - **Space efficiency: ** π΄
682+ - **Space efficiency: ** β
683683 - **Arbitrary data: ** π‘
684- - **Tidy data: ** π΄
685- - **Array data: ** π΄
686- - **Long term archival/sharing: ** π’
684+ - **Tidy data: ** β
685+ - **Array data: ** β
686+ - **Long term archival/sharing: ** β
687687 - **Best use cases: ** Saving nested/relational data, storing web requests.
688688
689689JSON is a popular human-readable data format.
@@ -712,11 +712,11 @@ Excel
712712
713713 - **Type **: Text format
714714 - **Packages needed: ** `openpyxl <https://openpyxl.readthedocs.io/en/stable/ >`__
715- - **Space efficiency: ** π΄
716- - **Arbitrary data: ** π΄
715+ - **Space efficiency: ** β
716+ - **Arbitrary data: ** β
717717 - **Tidy data: ** π‘
718- - **Array data: ** π΄
719- - **Long term archival/sharing: ** π’
718+ - **Array data: ** β
719+ - **Long term archival/sharing: ** β
720720 - **Best use cases: ** Sharing data in many fields. Quick data analysis.
721721
722722Excel is very popular in social sciences and economics.
@@ -735,9 +735,9 @@ Graph formats (adjency lists, gt, GraphML etc.)
735735 - **Type **: Many different formats
736736 - **Packages needed: ** Depends on a format.
737737 - **Space efficiency: ** π‘
738- - **Arbitrary data: ** π΄
739- - **Tidy data: ** π΄
740- - **Array data: ** π΄
738+ - **Arbitrary data: ** β
739+ - **Tidy data: ** β
740+ - **Array data: ** β
741741 - **Long term archival/sharing: ** π‘
742742 - **Best use cases: ** Saving graphs or data that can be represented as a graph.
743743
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