I'm trying to recreate the first panel.interact example in the Holoviz tutorial using a Pandas dataframe instead of a Dask dataframe. I get the slider, but the pandas dataframe row does not show.
See the original example at: http://holoviz.org/tutorial/Building_Panels.html
I've tried using Dask as in the Holoviz example. Dask rows print out just fine, but it demonstrates that panel seem to treat Dask dataframe rows differently for printing than Pandas dataframe rows. Here's my minimal code:
import pandas as pd
import panel
l1 = ['a','b','c','d','a','b']
l2 = [1,2,3,4,5,6]
df = pd.DataFrame({'cat':l1,'val':l2})
def select_row(rowno=0):
row = df.loc[rowno]
return row
panel.extension()
panel.extension('katex')
panel.interact(select_row, rowno=(0, 5))
I've included a line with the katex extension, because without it, I get a warning that it is needed. Without it, I don't even get the slider.
I can call the select_row(rowno=0) function separately in a Jupyter cell and get a nice printout of the row, so it appears the function is working as it should.
Any help in getting this to work would be most appreciated. Thanks.
Got a solution. With Pandas, loc[rowno:rowno] returns a pandas.core.frame.DataFrame object of length 1 which works fine with panel while loc[rowno] returns a pandas.core.series.Series object which does not work so well. Thus modifying the select_row() function like this makes it all work:
def select_row(rowno=0):
row = df.loc[rowno:rowno]
return row
Still not sure, however, why panel will print out the Dataframe object and not the Series object.
Note: if you use iloc, then you use add +1, i.e., df.iloc[rowno:rowno+1].
Related
I am working on the Jupyter notebook and have been facing issues in increasing the length of the output of the Jupyter Notebook. I can see the output as follows:
I tried increasing the default length of the columns in pandas with no success. Can you please help me with it?
If you were using the typical way to view a dataframe in Jupyter (see my puzzelment about your screenshot in my comments to your original post) it would be things like this:
adapted from answer to 'Pretty-print an entire Pandas Series / DataFrame'
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
display(df)
(Note that will work with the text-based viewing, too. Note it uses print(df) in the answer to 'Pretty-print an entire Pandas Series / DataFrame'.
Adjust the 'display.max_colwidth' if you want the entire column text to show:
with pd.option_context('display.max_rows', None, 'display.max_columns', None,'display.max_colwidth', -1):
display(df)
(If you prefer text like you posted, replace display() with print()
Generally with the solutions above the view window in Jupyter will get scrollbars so you can navigate to view all still.
You can also set the number of rows to show to be lower to save space, see example here.
You may also be interested in Pandas dataframe hide index functionality? or Using python / Jupyter Notebook, how to prevent row numbers from printing?.
As pointed out here, setting some some global options is covered in the Pandas Documentation for top-level options.
For display() to work these days you don't need to do anything extra. But if your are using old Jupyter or it doesn't work then try adding towards the top of your notebook file and running the following as a cell first:
from IPython.display import display
I am building an application that displays stock correlations data in various visual forms, including a matrix with a heatmap applied. My heatmap is created by passing the correlation matrix dataframe into IPy Widgets Output, so I can display it as part of a VBox later on. I have successfully applied a background gradient and formatted my numbers to 2dp. Can anyone help me edit the function to also reduce the font size, I just want to shrink it up a little?
Note: I chose to do this using dataframe styling over matplotlib as I had a number of issues getting the output to display in the way I wanted. I also have a function that downloads the dataframe to excel with the styling applied.
I have tried putting the following line of code at the beginning of my notebook so I can leave it outside of the function, but it seems to get ignored once the dataframe is passed to Output.
pd.options.display.float_format = "{:,.2f}".format
Here is my code sample:
import seaborn as sns
import ipywidgets as ipw
import pandas as pd
import numpy as np
#Sample Data
data = np.random.randint(5,30,size=500)
df = pd.DataFrame(data.reshape((50,10)))
corr = df.corr()
#Function produces dataframe as Output
def output_heatmap_df(df):
out = ipw.Output()
with out:
display(df.style\
.background_gradient(cmap=sns.diverging_palette(220,10, as_cmap=True),axis=None).format("{:,.2f}"))
out.layout.width='1600px'
return out
output_heatmap_df(corr)
In case anyone should come across this, the below code worked for me in the end:
def output_heatmap_df(df):
out = ipw.Output()
with out:
display(df.style\
.background_gradient(cmap=sns.diverging_palette(220,10, as_cmap=True),axis=None).format("{:,.2f}")
.set_properties(**{'text-align':'center','font-size':'10px'})
.set_table_styles([{'selector':'th','props':[('text-align','center'),('font-size','10px')]}])
)
out.layout.width='1600px'
return out
I am working with time series data that is formatted as each row is a single instance of a ID/time/data. This means that the rows don't correspond 1 to 1 for each ID. Each ID has many rows across time.
I am trying to use dask delayed to have a function run on an entire ID sequence (it makes sense that the operation should be able to run on each individual ID at the same time since they don't affect each other). To do this I am first looping through each of the ID tags, pulling/locating all the data from that ID (with .loc in pandas, so it is a separate "mini" df), then delaying the function call on the mini df, adding a column with the delayed values and adding it to a list of all mini dfs. At the end of the for loop I want to call dask.compute() on all the mini-dfs at once but for some reason the mini df's values are still delayed. Below I will post some pseudocode about what I just tried to explain.
I have a feeling that this may not be the best way to go about this but it's what made sense at the time and I can't understand whats wrong so any help would be very much appreciated.
Here is what I am trying to do:
list_of_mini_dfs = []
for id in big_df:
curr_df = big_df.loc[big_df['id'] == id]
curr_df['new value 1'] = dask.delayed(myfunc)(args1)
curr_df['new value 2'] = dask.delayed(myfunc)(args2) #same func as previous line
list_of_mini_dfs.append(curr_df)
list_of_mini_dfs = dask.delayed(list_of_mini_dfs).compute()
Concat all mini dfs into new big df.
As you can see by the code I have to reach into my big/overall dataframe to pull out each ID's sequence of data since it is interspersed throughout the rows. I want to be able to call a delayed function on that single ID's data and then return the values from the function call into the big/overall dataframe.
Currently this method is not working, when I concat all the mini dataframes back together the two values I have delayed are still delayed, which leads me to think that it is due to the way I am delaying a function within a df and trying to compute the list of dataframes. I just can't see how to fix it.
Hopefully this was relatively clear and thank you for the help.
IIUC you are trying to do a sort of transform using dask.
import pandas as pd
import dask.dataframe as dd
import numpy as np
# generate big_df
dates = pd.date_range(start='2019-01-01',
end='2020-01-01')
l = len(dates)
out = []
for i in range(1000):
df = pd.DataFrame({"ID":[i]*l,
"date": dates,
"data0": np.random.randn(l),
"data1": np.random.randn(l)})
out.append(df)
big_df = pd.concat(out, ignore_index=True)\
.sample(frac=1)\
.reset_index(drop=True)
Now you want to apply your function fun on columns data0 and data1
Pandas
out = big_df.groupby("ID")[["data0","data1"]]\
.apply(fun)\
.reset_index()
df_pd = pd.merge(big_df, out, how="left", on="ID" )
Dask
df = dd.from_pandas(big_df, npartitions=4)
out = df.groupby("ID")[["data0","data1"]]\
.apply(fun, meta={'data0':'f8',
'data1':'f8'})\
.rename(columns={'data0': 'new_values0',
'data1': 'new_values1'})\
.compute() # Here you need to compute otherwise you'll get NaNs
df_dask = dd.merge(df, out,
how="left",
left_on=["ID"],
right_index=True)
The dask version is not necessarily faster than the pandas one. In particular if your df fits in RAM.
I have a Data Frame which looks like this:
I am trying to vectorize every row, but only from the text column. I wrote this code:
vectorizerCount = CountVectorizer(stop_words='english')
# tokenize and build vocab
allDataVectorized = allData.apply(vectorizerCount.fit_transform(allData.iloc[:]['headline_text']), axis=1)
The error says:
TypeError: ("'csr_matrix' object is not callable", 'occurred at index 0')
Doing some research and trying changes I found out the fit_transform function returns a scipy.sparse.csr.csr_matrix and that is not callable.
Is there another way to do this?
Thanks!
There are a number of problems with your code. You probably need something like
allDataVectorized = pd.DataFrame(vectorizerCount.fit_transform(allData[['headline_text']]))
allData[['headline_text']]) (with the double brackets) is a DataFrame, which transforms to a numpy 2d array.
fit_transform returns a csr matrix.
pd.DataFrame(...) creates a DataFrame from a csr matrix.
The math problem that I'm solving gives different analytical solutions in different scenarios, and I would like to summarize the result in a nice table. IPython Notebook renders the list nicely:
for example:
import sympy
from pandas import DataFrame
from sympy import *
init_printing()
a, b, c, d = symbols('a b c d')
t = [[a/b, b/a], [c/d, d/c]]
t
However, when I summarize the answers into a table using DataFrame, the math cannot be rendered any more:
df = DataFrame(t, index=['Situation 1', 'Situation 2'], columns=['Answer1','Answer2'])
df
"print df.to_latex()" also gives the same result. I also tried "print(latex(t))" but it gives this after compiling in LaTex, which is alright, but I still need to manually convert it to a table:
How should I use DataFrame properly in order to render the math properly? Or is there any other way to export the math result into a table in Latex? Thanks!
Update: 01/25/14
Thanks again to #Jakob for solving the problem. It works perfectly for simple matrices, though there are still some minor problems for more complicated math expressions. But I guess like #asmeurer said, perfection requires an update in IPython and Pandas.
Update: 01/26/14
If I render the result directly, i.e. just print the list, it works fine:
MathJax is currently not able to render tables, hence the most obvious approach (pure latex) does not work.
However, following the advise of #asmeurer you should use an html table and render the cell content as latex. In your case this could be easily achieved by the following intermediate step:
from sympy import latex
tl = map(lambda tc: '$'+latex(tc)+'$',t)
df = DataFrame(tl, index=['Situation 1', 'Situation 2'], columns=['Answer'])
df
which gives:
Update:
In case of two dimensional data, the simple map function will not work directly. To cope with this situation the numpy shape, reshape and ravel functions could be used like:
import numpy as np
t = [[a/b, b/a],[a*a,b*b]]
tl=np.reshape(map(lambda tc: '$'+latex(tc)+'$',np.ravel(t)),np.shape(t))
df = DataFrame(tl, index=['Situation 1', 'Situation 2'], columns=['Answer 1','Answer 2'])
df
This gives:
Update 2:
Pandas crops cell content if the string length exceeds a certain number. E.g a more complicated expression like
t1 = [a/2+b/2+c/2+d/2]
tl=np.reshape(map(lambda tc: '$'+latex(tc)+'$',np.ravel(t1)),np.shape(t1))
df = DataFrame(tl, index=['Situation 1'], columns=['Answer 1'])
df
gives:
To cope with this issue a pandas package option has to be altered, for details see here. For the present case the max_colwidth has to be changed. The default value is 50, hence let's change it to 100:
import pandas as pd
pd.options.display.max_colwidth=100
df
gives: