python-ggplot

I need to plot 3 columns of a Pandas dataframe on python ggplot, with the same index. Is that possible? Thank you I'm assuming you want something in ggplot that replicates something like this in matplotlib. import pandas as pd df = pd.DataFrame({'a': range(10), 'b': range(5,15), 'c': range(7,17)}) df.plot() ggplot expects the data to be in 'long' format, so you need to do a little reshaping, with melt . It also currently does not support plotting the index, so that needs to made into a column. from ggplot import ggplot, geom_line, aes import pandas as pd df = pd.DataFrame({'a': range(10), 'b':

I'm using yhat's ggplot library . I have the following pandas DataFrame: degree observed percent observed expected percent expected 0 0 0 0.0 0 0.044551 1 1 1 0.1 1 0.138604 2 2 3 0.3 2 0.215607 3 3 4 0.4 2 0.223592 4 4 1 0.1 2 0.173905 5 5 1 0.1 1 0.108208 At the moment, I'm doing the following (where df returned in the first line in the first line in the function is the DataFrame above): def chartObservedExpected(graph): df = observedExpected(graph) return ggplot(aes(x='degree', y='percent observed'), data=df) + \ geom_point() + \ ylim(-0.015,1.015) + \ xlim(-0.05,max(df['degree']) + 0.25) +

My pandas DataFrame results_print has 2-dim arrays that are images. I print them like so: n_rows = results_print.shape[0] n_cols = results_print.shape[1] f, a = plt.subplots(n_cols, n_rows, figsize=(n_rows, n_cols)) methods = ['img', 'sm', 'rbd', 'ft', 'mbd', 'binary_sal', 'sal'] for r in range(n_rows): for c, cn in zip(range(len(methods)), methods): a[c][r].imshow(results_print.at[r,cn], cmap='gray') Now I created a python ggplot image object: gg = ggplot(aes(x='pixels'), data=DataFrame({'pixels': results_print.at[6,'mbd'].flatten()})) + \ geom_density(position='identity', stat='density') + \

I'm experimenting with pandas and non-matplotlib plotting. Good suggestions are here . This question regards yhat's ggplot and I am running into two issues. Plotting a series in pandas is easy. frequ.plot() I don't see how to do this in the ggplot docs. Instead I end up creating a dataframe: cheese = DataFrame({'time': frequ.index, 'count' : frequ.values}) ggplot(cheese, aes(x='time', y='count')) + geom_line() I would expect ggplot -- a project that has "tight integration with pandas" -- to have a way to plot a simple series. Second issue is I can't get stat_smooth() to display when the x axis

I have some data where I've manipulated the dataframe using the following code: import pandas as pd import numpy as np data = pd.DataFrame([[0,0,0,3,6,5,6,1],[1,1,1,3,4,5,2,0],[2,1,0,3,6,5,6,1],[3,0,0,2,9,4,2,1],[4,0,1,3,4,8,1,1],[5,1,1,3,3,5,9,1],[6,1,0,3,3,5,6,1],[7,0,1,3,4,8,9,1]], columns=["id", "sex", "split", "group0Low", "group0High", "group1Low", "group1High", "trim"]) data #remove all where trim == 0 trimmed = data[(data.trim == 1)] trimmed #create df with columns to be split columns = ['group0Low', 'group0High', 'group1Low', 'group1High'] to_split = trimmed[columns] to_split level