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Description:

dfvue 3.0

A simple GUI to view csv files

About dfvue
dfvue is a minimal GUI for a quick view of csv files. It uses an
input panel similar to Microsoft Excel to check visually that the csv
file is read correctly. It provides most options of pandas’ read_csv
method to be very versatile on the possible csv format.
dfvue is a Python script that can be called from within Python or
as a command line tool. It is not supposed to produce
publication-ready plots but rather provide a quick overview of the csv
file.
The complete documentation for dfvue is available from:

https://mcuntz.github.io/dfvue/

Quick usage guide
dfvue can be run from the command line:
dfvue csv_file.csv
or from within Python:
from dfvue import dfvue
dfvue('csv_file.csv')
where the csv file is optional. The latter can be left out and a csv
file can be opened with the “Open File” button from within dfvue.
Note, dfvue uses the TkAgg backend of matplotlib. It must be
called before any other call to matplotlib. This also means that you
cannot launch it from within iPython if it was launched with
–pylab. It can be called from within a standard iPython, though,
or using ipython –gui tk.

General layout
On opening, dfvue presents currently only one panel for producing
scatter/line plots. This is the look in macOS light mode (higher
resolution images can be found in the documentation):

The pane is organised in this fashion: the plotting canvas, the
Matplotlib navigation toolbar and the pane, where one can choose the
plotting variables and plotting options. You can open another,
identical window for the same csv file with the button “New Window” on
the top right. You can then also read in a new csv file in one of the
windows with the button “Open File”.

Reading a csv file
The “Read csv file” window opens when a csv file is given.

The csv file can be given on the command line:
dfvue csv_file.csv
from within Python:
from dfvue import dfvue
dfvue('csv_file.csv')
or being selected from the “Choose csv file” selector that opens when
hitting the button “Open File”.
The “Read csv file” window reads the first 40 rows of the csv file
with pandas’ read_csv method using the options given in the pane. It
shows the resulting pandas.DataFrame in tabulated format. Changing
focus from one option entry to another, for example by hitting the
<tab> key, re-reads the first 40 rows of the csv file with
pandas.read_csv using the selected options in the form. Hitting
<enter> or <return> within the window reads the entire csv file using
the selected options and returns to the plotting panels. This is the
same than pressing the “Read csv” button in the lower right corner.
The options in the form are pandas’ read_csv default options except
for parse_date, which is set to True instead of False
here. Hover over the entry boxes to see explanations of the options in
the tooltip.
If the csv file includes a Date/Time column, it is best to set this
column as the index of the pandas.DataFrame by using
index_col. Correct datetime is indicated if the index has the data
type datetime64[ns] in the plot panels. This is then correctly
interpreted by the underlying Matplotlib when plotting, zooming, or
panning the axes.
missing_value is not an option of pandas’ read_csv. It is here for
convenience and any number entered in missing_value will be added to
pandas na_values.

Reading a csv file with options on the command line
The following options of pandas.read_csv can be given on the command line:
-s separator, --sep separator
Delimiter to use.
-i columns, --index_col columns
Column(s) to use as index, either given as column index
or string name.
-k rows, --skiprows rows
Line number(s) to skip (0-indexed, must include comma,
e.g. "1," for skipping the second row) or number of lines
to skip (int, without comma) at the start of the file.
-p bool/list/dict, --parse_dates bool/list/dict
boolean, if True -> try parsing the index.
list of int or names, e.g. 1,2,3
-> try parsing columns 1, 2, and 3 each as a separate
date column.
list of lists, e.g. [1,3]
-> combine columns 1 and 3 and parse as a single
date column.
dict, e.g. "foo":[1,3]
-> parse columns 1 and 3 as date and call result "foo"
-d format_string, --date_format format_string
Will parse dates according to this format.
For example: "%Y-%m-%d %H:%M%S". See
https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
-m missing_value, --missing_value missing_value
Missing or undefined value set to NaN. For negative values,
use long format, e.g. --missing_value=-9999.

Examples of pandas.read_csv options
Here are some examples of csv files and the options for
pandas.read_csv.
The most simple csv file would be like:
DATETIME,TA_1_1_1,RH_1_1,ALB_1_1_1
2015-01-01 00:30:00,-2.17794549084,97.2958103396,0.0
2015-01-01 01:00:00,-2.02584908489,98.2103903979,0.0
This can simply be read by setting index_col=0. The first column
including date and time can simply a be a ISO8601 date, for example
“2015-01-01 00:30:00” or “2015-01-01T00:30:00”, or be given by
date_format, which would be “%Y-%m-%d %H:%M:%S” in this case. See
the documentation of pandas.to_datetime or strftime.
Command line options would be:

dfvue -i 0 csv-file

or

dfvue -i 0 -d “%Y-%m-%d %H:%M:%S” csv-file

A common practice is to put a special value for measurement errors or
similar such as -9999:
DATETIME,TA_1_1_1,RH_1_1,ALB_1_1_1
2015-01-01 00:30:00,-2.17794549084,97.2958103396,-9999
2015-01-01 01:00:00,-2.02584908489,98.2103903979,-9999
This can be read by setting missing_value=-9999. On the command
line, this is:

dfvue -i 0 –missing_value=-9999 csv-file

or

dfvue -i 0 -d “%Y-%m-%d %H:%M:%S” –missing_value=-9999 csv-file

You have to use the long form –missing_value=-9999 instead of the
short form -m -9999 in case of negative missing values because the
command line would interpret -9999 as a separate option in the
second case and would fail.
Date and time information can be given in different formats, for example:
Date;rho H1 (kg/m3);alb H1 (-);T_Psy H1 (degC);WS_EC H1 (m/s);Prec H1 (mm/30min)
01.01.2015 00:30;97.2958103396;-9999;-2.17794549084
01.01.2015 01:00;98.2103903979,-9999;-2.02584908489
which can be read by setting the date format:
date_format=%d.%m.%Y %H:%M, index_col=0, missing_value=-9999, as
well as the field separator sep=;. On the the command line, this is:

dfvue -s “;” -i 0 -d “%d.%m.%Y %H:%M” –missing_value=-9999 csv-file

Or in FLUXNET / ICOS / europe-fluxdata.eu format with a
second row that shows the variable units:
TIMESTAMP_END,TA_1_1_1,RH_1_1_1,ALB_1_1_1
YYYYMMDDhhmm,degC,%,adimensional
201501010030,-2.17794549084,97.2958103396,-9999
201501010100,-2.02584908489,98.2103903979,-9999
which is read with date_format=%Y%M%d%H%M, index_col=0,
skiprows=1,, and missing_value=-9999. Note the comma after “1” in
skiprows. Without the command, skiprows would be the number of rows
to skip at the beginning, i.e. the first row, which would be
wrong. The comma indicates that skiprows is a list and hence a list
of row indexes, that means 1 here and thus skip the second row. This
would be on the command line

dfvue -i 0 -d “%Y%m%d%H%M” –skiprows=1, –missing_value=-9999 csv-file

Date and time information can also be in different columns. Here the
second column is the day-of-the-year:
year,jday,hour,min,tair,rhair,albedo
2015,1,0,30,-2.17794549084,97.2958103396,-9999
2015,1,1,0,-2.02584908489,98.2103903979,-9999
which can be read by setting parse_dates=[0,1,2,3], index_col=0,
and date_format=%Y %j %H %M, as well as missing_value=-9999. Note
the brackets “[]” around parse_dates. Without brackets it would
parse columns 0, 1, 2, and 3 each as a separate date column, whereas
with brackets it combines columns 0, 1, 2, and 3 and parses it as a
single date column, with index “0”. It will use a space between column
entries. Hence index_col=0 sets this combined column as the index,
parsing the dates with the format “%Y %j %H %M” with spaces between
the strftime formats.
On the command line, this would be:

dfvue -i 0 -p [0,1,2,3] -d “%Y %j %H %M” –missing_value=-9999 csv-file

If you want to have spaces in the list of parse_dates on the command
line, you have to use the long form: –parse_dates=”[0, 1, 2, 3]”.

Scatter/Line panel
Here is the Scatter/Line panel in macOS dark mode, describing all
buttons, sliders, entry boxes, spinboxes, and menus:

The default plot is a line plot with solid lines (line style ‘ls’ is
‘-‘). One can set line style ‘ls’ to None and set a marker symbol,
e.g. ‘o’ for circles, to get a scatter plot. A large variety of line
styles, marker symbols and color notations are supported.

Installation
dfvue is an application written in Python. It can be installed
with pip:
python -m pip install dfvue
or via Conda:
conda install -c conda-forge dfvue
dfvue uses CustomTkinter if it is installed. CustomTkinter is not on Conda. One might install dfvue with pip in a conda environment to use CustomTkinter:
conda install pip
python -m pip install dfvue
It this looks ugly on Linux (see this thread), pip uninstall
customtkinter, or pip uninstall dfvue and reinstall it with conda,
which then uses the Azure theme by rdbende.

License
dfvue is distributed under the MIT License. See the LICENSE file
for details.
Copyright (c) 2023- Matthias Cuntz
dfvue uses CustomTkinter by Tom Schimansky if installed, and
otherwise the Azure theme (v2.0) by rdbende, for example in conda
environments.