pandas


Convert multiple datatype to float?


Using pandas, how to convert multiple dateframe column of datatype "object" to float.
df = pd.DataFrame()
df["A"] = ["123.45","34","-9","4","5"]
df["B"] = ["-9.07","5.4","3","1.0","4.5557"]
df["C"] = ["34","34.98","-9.654","45","6"]
df["D"] = ["AAA","AVF","ERD","DFE","SFE"]
using this gives AttributeError: 'list' object has no attribute 'apply':
[df["A"],df["B"],df["C"]] = [df["A"],df["B"],df["C"]].apply(pd.to_numeric, errors='coerce')
df = df.apply(pd.to_numeric, errors='coerce')
In [119]: df
Out[119]:
A B C
0 123.45 -9.0700 34.000
1 34.00 5.4000 34.980
2 -9.00 3.0000 -9.654
3 4.00 1.0000 45.000
4 5.00 4.5557 6.000
In [120]: df.dtypes
Out[120]:
A float64
B float64
C float64
dtype: object
UPDATE:
In [128]: df[df.columns.drop('D')] = df[df.columns.drop('D')].apply(pd.to_numeric, errors='coerce')
In [129]: df
Out[129]:
A B C D
0 123.45 -9.0700 34.000 AAA
1 34.00 5.4000 34.980 AVF
2 -9.00 3.0000 -9.654 ERD
3 4.00 1.0000 45.000 DFE
4 5.00 4.5557 6.000 SFE
In [130]: df.dtypes
Out[130]:
A float64
B float64
C float64
D object
dtype: object
UPDATE2:
In [143]: df[['A','B','C']] = df[['A','B','C']].apply(pd.to_numeric, errors='coerce')
In [144]: df
Out[144]:
A B C D
0 123.45 -9.0700 34.000 AAA
1 34.00 5.4000 34.980 AVF
2 -9.00 3.0000 -9.654 ERD
3 4.00 1.0000 45.000 DFE
4 5.00 4.5557 6.000 SFE
In [145]: df.dtypes
Out[145]:
A float64
B float64
C float64
D object
dtype: object

Related Links

mapping values from another pandas df
Tensorflow: Cannot allocate buffer larger than kint32max for StringOutputStream
using assign and lambda to combine year and month columns into 1 date column
Apply an element-wise function on a pandas dataframe with index and column values as inputs
How to pass dataset directory in google datalab
After rename column get keyerror
pandas groupby and mean aggregation on more columns
str.replace function creating NaN data
pandas element wise conditional return index
pandas series or tidy dataframe: index level values to dataframe columns
pyspark dataframe count distinct value row by row considering history
Pyspark - how to backfill a DataFrame?
TensorFlow input pipeline for deployment on CloudML
From Pandas groupBy to PySpark groupBy
Applying different formats to different columns dataframe
Remove none values from dataframe

Categories

HOME
layout
relative-path
iot
google-oauth
cmd
frameworks
callback
fingerprint
webrequest
handsontable
quickbooks
nstableview
size
ups
text-rendering
excel-vba-mac
conemu
orleans
captiveportal
restful-authentication
jndi
sqlcipher
centos6.5
chromebook
typo3-6.2.x
libssl
nat
lightswitch-2013
windows-dev-center
ghost4j
http-digest
stacked
dosbox
web-mining
hockeyapp
withings
webix-treetable
tasker
dism
sql-server-agent
janrain
glew
nand2tetris
vao
modelmapper
segment
apple-news
angular-resource
strptime
zip4j
theming
clean-architecture
idisposable
jxcore
nodebb
pearson
smart-table
pintos
infix-notation
skype4py
qcustomplot
livequery
hsv
xna-4.0
cyclomatic-complexity
feedback
iis-arr
pundit
muse
operation
sniffer
inmobi
clicktag
javafx-webengine
rdtsc
wordpress-theme-customize
android-radiobutton
picturefill
srs
mcts
yui-compressor
plasma
html-editor
ocunit
cassini-dev
funscript
newtonscript
nsmanagedobject
gnu-prolog
krl
inotifycollectionchanged
digest-authentication
dmx512
getresponsestream
mediarss
scripting-languages
defensive-programming

Resources

Mobile Apps Dev
Database Users
javascript
java
csharp
php
android
MS Developer
developer works
python
ios
c
html
jquery
RDBMS discuss
Cloud Virtualization
Database Dev&Adm
javascript
java
csharp
php
python
android
jquery
ruby
ios
html
Mobile App
Mobile App
Mobile App