python - Want to plot Pandas Dataframe as Multiple Histograms with log10 scale x-axis -


i have floating point data in pandas dataframe. each column represents variable (they have string names) , each row set of values (the rows have integer names not important). 

>>> print data 0      kppawr23    kppaspyd 1      3.312387   13.266040 2      2.775202    0.100000 3    100.000000  100.000000 4    100.000000   39.437420 5     17.017150   33.019040 ...

i want plot histogram each column. best result have achieved hist method of dataframe:

data.hist(bins=20)

but want x-axis of each histogram on log10 scale. , bins on log10 scale too, easy enough bins=np.logspace(-2,2,20).

a workaround might log10 transform data before plotting, approaches have tried,

data.apply(math.log10)

and

data.apply(lambda x: math.log10(x))

give me floating point error.

    "cannot convert series {0}".format(str(converter))) typeerror: ("cannot convert series <type 'float'>", u'occurred @ index kppawr23')

you use

ax.set_xscale('log')

data.hist() returns array of axes. you'll need call ax.set_xscale('log') each axes, ax make each of logarithmically scaled.

for example,

import numpy np import pandas pd import matplotlib.pyplot plt np.random.seed(2015)  n = 100 arr = np.random.random((n,2)) * np.logspace(-2,2,n)[:, np.newaxis] data = pd.dataframe(arr, columns=['kppawr23', 'kppaspyd'])  bins = np.logspace(-2,2,20) axs = data.hist(bins=bins) ax in axs.ravel():     ax.set_xscale('log')  plt.gcf().tight_layout() plt.show()

yields

enter image description here

by way, take log of every value in dataframe, data, use 

logdata = np.log10(data)

because numpy ufuncs (such np.log10) can applied pandas dataframes because operate elementwise on values in dataframe.

data.apply(math.log10) did not work because apply tries pass entire column (a series) of values math.log10. math.log10 expects scalar value only.

data.apply(lambda x: math.log10(x)) fails same reason data.apply(math.log10) does. moreover, if data.apply(func) , data.apply(lambda x: func(x)) both viable options, first should preferred since lambda function make call tad slower.

you use data.apply(np.log10), again since numpy ufunc np.log10 can applied series, there no reason bother doing when np.log10(data) works.

you use data.applymap(math.log10) since applymap calls math.log10 on each value in data one-at-a-time. far slower calling equivalent numpy function, np.log10 on entire dataframe. still, worth knowing applymap in case need call custom function not ufunc.


Comments

Post a Comment

Popular posts from this blog

java - Spring Data JPA: Why findOne(id) executing delete query internally? -

i have 1 application using @manytomany annotation link 2 different entities 1 parent entity. not sure if have implemented properly. when execute .findone() on parent entity's repository find it, @ first fetch data along executing delete queries internally delete record mapping table (a table jpa creates after adding @manytomany annotation.). following generated sql queries , code snippet: java class @entity @table(name = "inventory") public class inventory { @id long id; // many other properties @manytomany(fetch=fetchtype.lazy) private list<stream> streams; @manytomany(fetch=fetchtype.lazy) private list<subject> subjects; } there no other annotation i've put in stream , subject entity class link inventory class. when run app, create following tables: inventory stream subject inventory_stream inventory_subject (inventory_stream , inventory_subject, 2 tables created automatically, don't k...
Read more

python - Mongodb How to add addtional information when aggregating? -

i beginner , wrote line pipeline works, wanna add other information output, screen name , or number of tweets.i tried add under $group gave me syntax error everytime here pipeline: def make_pipeline(): # complete aggregation pipeline pipeline = [ { '$match': { "user.statuses_count": {"$gt":99 }, "user.time_zone": "brasilia" } }, { "$group": { "_id": "$user.id", "followers": { "$max": "$user.followers_count" } } }, { "$sort": { "followers": -1 } }, { "$limit" : 1 } ]; i using on example : { "_id" : objectid("5304e2e3cc9e684aa98bef97"), "text" : "first week of school on :p", "in_reply_to_status_id" : null, "retweet_count" : null, ...
Read more

java - Incorrect order of records in M-M relationship in hibernate -

Image
i have 4 records in bridge table in mysql database. using @manytomany in hibernate, accurate order of employees incorrect incorrect of projects. following required material figure out doing mistake. this source. @entity @table(name="project") public class projectbean { @id @generatedvalue @column(name="project_id") private int projectid; @column(name="title") private string projecttitle; @manytomany(mappedby="projects") private collection<employeebean> employees; public projectbean() { employees = new arraylist<employeebean>(); } //getters & setters } @entity @table(name="employee") public class employeebean { @id @generatedvalue @column(name="employee_id") private int employee_id; @column(name="first_name") private string ...
Read more