I am transitioning from R to Python. I just began using Pandas. I have an R code that subsets nicely:
k1 <- subset(data, Product = p.id & Month < mn & Year == yr, select = c(Time, Product))
Now, I want to do similar stuff in Python. this is what I have got so far:
import pandas as pd data = pd.read_csv("../data/monthly_prod_sales.csv") #first, index the dataset by Product. And, get all that matches a given 'p.id' and time. data.set_index('Product') k = data.ix[[p.id, 'Time']] # then, index this subset with Time and do more subsetting..
I am beginning to feel that I am doing this the wrong way. perhaps, there is an elegant solution. Can anyone help? I need to extract month and year from the timestamp I have and do subsetting. Perhaps there is a one-liner that will accomplish all this:
k1 <- subset(data, Product = p.id & Time >= start_time & Time < end_time, select = c(Time, Product))
thanks.
Subsetting a data frame is the process of selecting a set of desired rows and columns from the data frame. You can select: all rows and limited columns. all columns and limited rows. limited rows and limited columns.
I'll assume that Time
and Product
are columns in a DataFrame
, df
is an instance of DataFrame
, and that other variables are scalar values:
For now, you'll have to reference the DataFrame
instance:
k1 = df.loc[(df.Product == p_id) & (df.Time >= start_time) & (df.Time < end_time), ['Time', 'Product']]
The parentheses are also necessary, because of the precedence of the &
operator vs. the comparison operators. The &
operator is actually an overloaded bitwise operator which has the same precedence as arithmetic operators which in turn have a higher precedence than comparison operators.
In pandas
0.13 a new experimental DataFrame.query()
method will be available. It's extremely similar to subset modulo the select
argument:
With query()
you'd do it like this:
df[['Time', 'Product']].query('Product == p_id and Month < mn and Year == yr')
Here's a simple example:
In [9]: df = DataFrame({'gender': np.random.choice(['m', 'f'], size=10), 'price': poisson(100, size=10)}) In [10]: df Out[10]: gender price 0 m 89 1 f 123 2 f 100 3 m 104 4 m 98 5 m 103 6 f 100 7 f 109 8 f 95 9 m 87 In [11]: df.query('gender == "m" and price < 100') Out[11]: gender price 0 m 89 4 m 98 9 m 87
The final query that you're interested will even be able to take advantage of chained comparisons, like this:
k1 = df[['Time', 'Product']].query('Product == p_id and start_time <= Time < end_time')
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