Python / Pandas - GUI for viewing a DataFrame or Matrix [closed]

Answer 1

The nicest solution I've found is using qgrid (see here, and also mentioned in the pandas docs). You can install by

pip install qgrid

and then you need to do a further install (just once) in your IPython notebook

qgrid.nbinstall()

Afterwards, it's as easy as taking your pandas df and running

qgrid.show_grid(df)

The other nice thing is that it renders in nbviewer too. See it in action here

Answer 2

I highly recommend you use QTableView not QTableWidget. QTableView is based on model-view programming.

There are 2 different ways how these widgets can access their data. The traditional way involves widgets which include internal containers for storing data. This approach is very intuitive, however, in many non-trivial applications, it leads to data synchronization issues. The second approach is model/view programming, in which widgets do not maintain internal data containers

I wrote a model for pandas dataframe.

# -*- coding: utf-8 -*-
from PyQt5 import QtCore
from PyQt5 import QtWidgets
from PyQt5 import QtGui
import matplotlib.pyplot as plt

class PandasModel(QtCore.QAbstractTableModel):
    """
    Class to populate a table view with a pandas dataframe
    """

    def __init__(self, data, parent=None):
        QtCore.QAbstractTableModel.__init__(self, parent)
        self._data = data

    def rowCount(self, parent=None):
        return len(self._data.values)

    def columnCount(self, parent=None):
        return self._data.columns.size

    def data(self, index, role=QtCore.Qt.DisplayRole):
        if index.isValid():
            if role == QtCore.Qt.DisplayRole:
                if(index.column() != 0):
                    return str('%.2f'%self._data.values[index.row()][index.column()])
                else:
                    return str(self._data.values[index.row()][index.column()])
        return None

    def headerData(self, section, orientation, role):
        if orientation == QtCore.Qt.Horizontal and role == QtCore.Qt.DisplayRole:
            return self._data.columns[section]
        elif orientation == QtCore.Qt.Vertical and role == QtCore.Qt.DisplayRole:
            return str(self._data.index[section])
        return None

    def flags(self, index):
        flags = super(self.__class__,self).flags(index)
        flags |= QtCore.Qt.ItemIsSelectable
        flags |= QtCore.Qt.ItemIsEnabled
        return flags


if __name__=='__main__':
    import pandas as pd
    import numpy as np
    df = pd.DataFrame()
    df['Field1']=np.arange(0,10,.5)
    df['Field2']=np.arange(0,10,.5)
    app = QtWidgets.QApplication([])
    table = QtWidgets.QTableView()
    mymodel = PandasModel(df)
    table.setModel(mymodel)
    table.show()
    app.exec_()

You can easily change the model to edit or show the elements nicely based on your need. For more information refer to modelview

Answer 3

One very simple way is to use xlwings to view the dataframe in Excel. It has excellent treatment of Pandas dataframes. Install as usual using

pip install xlwings

then just have Excel open while you are working and

import xlwings as xw
xw.sheets.active.range("A1").value = df         # put df in currently active sheet

More sophisticated you can use app = xw.App() to open Excel and/or xw.Book() to create a new workbook from Excel. To create/switch between sheets I usually use a small custom function

def addActivate(wb, sheet, after=None, before=None):
    try:
        wb.sheets.add(sheet, after=after, before=before)
    except ValueError:
        wb.sheets(sheet).activate()

addActivate(xw.sheets.active, "newSheet")
xw.sheets.active.range("A1") = df

Answer 4

you can also use pandastable library

https://github.com/dmnfarrell/pandastable

I found it very useful for my application

you can simply install pandastable using 'pip install pandastable'

my application works on pandas==0.23.4 and this version of pandas works well with pandastable

Answer 5

Can I propose pivotablejs?

It provides horizontal and vertical pivoting, filtering, graphing, sorting, and lots of different aggregations all in just a few lines in a Jupyter notebook (tip: right-click the [pop out] link and open in a new tab for increased flexibility)

!pip install pivottablejs
from pivottablejs import pivot_ui

pivot_ui(df, outfile_path='pivottablejs.html')

https://towardsdatascience.com/two-essential-pandas-add-ons-499c1c9b65de

Answer 6

I use QTableWidget from PyQt to display a DataFrame. I create a QTableWidgetObject and then populate with QTableWidgetItems created with DataFrame values. Following is the snippet of code that reads a CSV file ,create a DataFrame, then display in a GUI:

df  = read_csv(filename, index_col = 0,header = 0)
self.datatable = QtGui.QTableWidget(parent=self)
self.datatable.setColumnCount(len(df.columns))
self.datatable.setRowCount(len(df.index))
for i in range(len(df.index)):
    for j in range(len(df.columns)):
        self.datatable.setItem(i,j,QtGui.QTableWidgetItem(str(df.iget_value(i, j))))

Update:

As this answer was quite old, it deserves an update. There are many options available now to view the dataframes in GUI.

1. As others have pointed out, Python IDEs such as Spyder come with dataframe viewers.
2. qgrid is another option for the jupyter notebook widget that renders the dataframes within the notebook.

If someone still wants to code a simple GUI to view the dataframes within Jupyter, following is the complete , minimal example using Pyqt5 .

%gui qt5 
from PyQt5.QtWidgets import QWidget,QScrollArea, QTableWidget, QVBoxLayout,QTableWidgetItem
import pandas as pd

win = QWidget()
scroll = QScrollArea()
layout = QVBoxLayout()
table = QTableWidget()
scroll.setWidget(table)
layout.addWidget(table)
win.setLayout(layout)    


df = pd.DataFrame({"a" : [4 ,5, 6],"b" : [7, 8, 9],"c" : [10, 11, 12]},index = [1, 2, 3])
table.setColumnCount(len(df.columns))
table.setRowCount(len(df.index))
for i in range(len(df.index)):
    for j in range(len(df.columns)):
        table.setItem(i,j,QTableWidgetItem(str(df.iloc[i, j])))

win.show()