How to Save the Pandas Dataframe/Series Data as a Figure

How to save the Pandas dataframe/series data as a figure?

Option-1: use matplotlib table functionality, with some additional styling:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.DataFrame()
df['date'] = ['2016-04-01', '2016-04-02', '2016-04-03']
df['calories'] = [2200, 2100, 1500]
df['sleep hours'] = [8, 7.5, 8.2]
df['gym'] = [True, False, False]

def render_mpl_table(data, col_width=3.0, row_height=0.625, font_size=14,
                     header_color='#40466e', row_colors=['#f1f1f2', 'w'], edge_color='w',
                     bbox=[0, 0, 1, 1], header_columns=0,
                     ax=None, **kwargs):
    if ax is None:
        size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array([col_width, row_height])
        fig, ax = plt.subplots(figsize=size)
        ax.axis('off')
    mpl_table = ax.table(cellText=data.values, bbox=bbox, colLabels=data.columns, **kwargs)
    mpl_table.auto_set_font_size(False)
    mpl_table.set_fontsize(font_size)

    for k, cell in mpl_table._cells.items():
        cell.set_edgecolor(edge_color)
        if k[0] == 0 or k[1] < header_columns:
            cell.set_text_props(weight='bold', color='w')
            cell.set_facecolor(header_color)
        else:
            cell.set_facecolor(row_colors[k[0]%len(row_colors) ])
    return ax.get_figure(), ax

fig,ax = render_mpl_table(df, header_columns=0, col_width=2.0)
fig.savefig("table_mpl.png")

Options-2 Use Plotly + kaleido

import plotly.figure_factory as ff
import pandas as pd

df = pd.DataFrame()
df['date'] = ['2016-04-01', '2016-04-02', '2016-04-03']
df['calories'] = [2200, 2100, 1500]
df['sleep hours'] = [8, 7.5, 8.2]
df['gym'] = [True, False, False]

fig =  ff.create_table(df)
fig.update_layout(
    autosize=False,
    width=500,
    height=200,
)
fig.write_image("table_plotly.png", scale=2)
fig.show()

For the above, the font size can be changed using the font attribute:

fig.update_layout(
    autosize=False,
    width=500,
    height=200,
    font={'size':8}
)

How to save a pandas DataFrame table as a png

Pandas allows you to plot tables using matplotlib (details here).
Usually this plots the table directly onto a plot (with axes and everything) which is not what you want. However, these can be removed first:

import matplotlib.pyplot as plt
import pandas as pd
from pandas.table.plotting import table # EDIT: see deprecation warnings below

ax = plt.subplot(111, frame_on=False) # no visible frame
ax.xaxis.set_visible(False)  # hide the x axis
ax.yaxis.set_visible(False)  # hide the y axis

table(ax, df)  # where df is your data frame

plt.savefig('mytable.png')

The output might not be the prettiest but you can find additional arguments for the table() function here.
Also thanks to this post for info on how to remove axes in matplotlib.

---

EDIT:

Here is a (admittedly quite hacky) way of simulating multi-indexes when plotting using the method above. If you have a multi-index data frame called df that looks like:

first  second
bar    one       1.991802
       two       0.403415
baz    one      -1.024986
       two      -0.522366
foo    one       0.350297
       two      -0.444106
qux    one      -0.472536
       two       0.999393
dtype: float64

First reset the indexes so they become normal columns

df = df.reset_index() 
df
    first second       0
0   bar    one  1.991802
1   bar    two  0.403415
2   baz    one -1.024986
3   baz    two -0.522366
4   foo    one  0.350297
5   foo    two -0.444106
6   qux    one -0.472536
7   qux    two  0.999393

Remove all duplicates from the higher order multi-index columns by setting them to an empty string (in my example I only have duplicate indexes in "first"):

df.ix[df.duplicated('first') , 'first'] = '' # see deprecation warnings below
df
  first second         0
0   bar    one  1.991802
1          two  0.403415
2   baz    one -1.024986
3          two -0.522366
4   foo    one  0.350297
5          two -0.444106
6   qux    one -0.472536
7          two  0.999393

Change the column names over your "indexes" to the empty string

new_cols = df.columns.values
new_cols[:2] = '',''  # since my index columns are the two left-most on the table
df.columns = new_cols 

Now call the table function but set all the row labels in the table to the empty string (this makes sure the actual indexes of your plot are not displayed):

table(ax, df, rowLabels=['']*df.shape[0], loc='center')

et voila:

Your not-so-pretty but totally functional multi-indexed table.

EDIT: DEPRECATION WARNINGS

As pointed out in the comments, the import statement for table:

from pandas.tools.plotting import table

is now deprecated in newer versions of pandas in favour of:

from pandas.plotting import table 

EDIT: DEPRECATION WARNINGS 2

The ix indexer has now been fully deprecated so we should use the loc indexer instead. Replace:

df.ix[df.duplicated('first') , 'first'] = ''

with

df.loc[df.duplicated('first') , 'first'] = ''

Save the Out[] table of a pandas dataframe as a figure

Here is a somewhat hackish solution but it gets the job done. You wanted a .pdf but you get a bonus .png. :)

import numpy as np
import pandas as pd
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt

from PySide.QtGui import QImage
from PySide.QtGui import QPainter
from PySide.QtCore import QSize
from PySide.QtWebKit import QWebPage

arrays = [np.hstack([ ['one']*3, ['two']*3]), ['Dog', 'Bird', 'Cat']*2]
columns = pd.MultiIndex.from_arrays(arrays, names=['foo', 'bar'])
df =pd.DataFrame(np.zeros((3,6)),columns=columns,index=pd.date_range('20000103',periods=3))

h = "<!DOCTYPE html> <html> <body> <p> " + df.to_html() + " </p> </body> </html>";
page = QWebPage()
page.setViewportSize(QSize(5000,5000))

frame = page.mainFrame()
frame.setHtml(h, "text/html")

img = QImage(1000,700, QImage.Format(5))
painter = QPainter(img)
frame.render(painter)
painter.end()
a = img.save("html.png")

pp = PdfPages('html.pdf')
fig = plt.figure(figsize=(8,6),dpi=1080) 
ax = fig.add_subplot(1, 1, 1)
img2 = plt.imread("html.png")
plt.axis('off')
ax.imshow(img2)
pp.savefig()
pp.close()

Edits welcome.

save a pandas.Series histogram plot to file

Use the Figure.savefig() method, like so:

ax = s.hist()  # s is an instance of Series
fig = ax.get_figure()
fig.savefig('/path/to/figure.pdf')

It doesn't have to end in pdf, there are many options. Check out the documentation.

Alternatively, you can use the pyplot interface and just call the savefig as a function to save the most recently created figure:

import matplotlib.pyplot as plt
s.hist()
plt.savefig('path/to/figure.pdf')  # saves the current figure

Plots from multiple columns

• Added from a comment toto_tico made on 2018-05-11
• If you are getting this error AttributeError: 'numpy.ndarray' object has no attribute 'get_figure', then it is likely that you are plotting multiple columns.
  
  • In this case, ax will be an array of all the axes.

ax = s.hist(columns=['colA', 'colB'])

# try one of the following
fig = ax[0].get_figure()
fig = ax[0][0].get_figure()

fig.savefig('figure.pdf')

How to save image created with 'pandas.DataFrame.plot'?

Try this :

fig = class_counts.plot(kind='bar',  
        figsize=(20, 16), fontsize=26).get_figure()

fig.savefig('test.pdf')

Convert pandas data frame to series

If you have a one column dataframe df, you can convert it to a series:

df.iloc[:,0]  # pandas Series

Since you have a one row dataframe df, you can transpose it so you're in the previous case:

df.T.iloc[:,0]

How to reversibly store and load a Pandas dataframe to/from disk

The easiest way is to pickle it using to_pickle:

df.to_pickle(file_name)  # where to save it, usually as a .pkl

Then you can load it back using:

df = pd.read_pickle(file_name)

Note: before 0.11.1 save and load were the only way to do this (they are now deprecated in favor of to_pickle and read_pickle respectively).

---

Another popular choice is to use HDF5 (pytables) which offers very fast access times for large datasets:

import pandas as pd
store = pd.HDFStore('store.h5')

store['df'] = df  # save it
store['df']  # load it

More advanced strategies are discussed in the cookbook.

---

Since 0.13 there's also msgpack which may be be better for interoperability, as a faster alternative to JSON, or if you have python object/text-heavy data (see this question).

Python pandas.io.data save plot?

pandas use matplotlib to draw so you can use this

import matplotlib.pyplot as plt

plt.savefig('image.png') # save to png
plt.savefig('image.pdf') # save to pdf

in your code

#pg 73 from Python in Finance
import numpy as np
import pandas as pd
import pandas.io.data as web
import matplotlib.pyplot as plt

sym1 = 'AAPL'
sym2 = 'FB'

symbol1 = web.DataReader(sym1, data_source='yahoo',start='1/1/2015', end='1/28/2016')
symbol2 = web.DataReader(sym2, data_source='yahoo',start='1/1/2015', end='1/28/2016')
ratio = symbol1; 
ratio['Close'] = symbol1['Close'] / symbol2['Close'];

#symbol1['Close'].plot(grid=True, figsize=(8, 5))
#symbol2['Close'].plot(grid=True, figsize=(8, 5))

ratio['Close'].plot(grid=True, figsize=(8, 5))

ratio['42d'] = np.round(pd.rolling_mean(ratio['Close'], window=42), 2)
ratio['252d'] = np.round(pd.rolling_mean(ratio['Close'], window=252), 2)
ratio[['Close', '42d', '252d']].plot(grid=True, figsize=(8, 5))

plt.savefig('foo.png') # save to png
plt.savefig('foo.png') # save to pdf
# plt.show() shows image

EDIT: see: https://stackoverflow.com/a/25588487/1832058

You can use

ax = df.plot() # your plot
fig = ax.get_figure()
fig.savefig('image.png')

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