Method 1: Select a single column at random In this approach firstly the Pandas package is read with which the given CSV file is imported using pd. read_csv() method is used to read the dataset. df. sample() method is used to randomly select rows and columns.
With pandas version 0.16.1
and up, there is now a DataFrame.sample
method built-in:
import pandas
df = pandas.DataFrame(pandas.np.random.random(100))
# Randomly sample 70% of your dataframe
df_percent = df.sample(frac=0.7)
# Randomly sample 7 elements from your dataframe
df_elements = df.sample(n=7)
For either approach above, you can get the rest of the rows by doing:
df_rest = df.loc[~df.index.isin(df_percent.index)]
Something like this?
import random
def some(x, n):
return x.ix[random.sample(x.index, n)]
Note: As of Pandas v0.20.0, ix
has been deprecated in favour of loc
for label based indexing.
sample
As of v0.20.0, you can use pd.DataFrame.sample
, which can be used to return a random sample of a fixed number rows, or a percentage of rows:
df = df.sample(n=k) # k rows
df = df.sample(frac=k) # int(len(df.index) * k) rows
For reproducibility, you can specify an integer random_state
, equivalent to using np.ramdom.seed
. So, instead of setting, for example, np.random.seed = 0
, you can:
df = df.sample(n=k, random_state=0)
The best way to do this is with the sample function from the random module,
import numpy as np
import pandas as pd
from random import sample
# given data frame df
# create random index
rindex = np.array(sample(xrange(len(df)), 10))
# get 10 random rows from df
dfr = df.ix[rindex]
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