I'm working with a dataframe of chemical formulas (str objects). Example
formula
Na0.2Cl0.4O0.7Rb1
Hg0.04Mg0.2Ag2O4
Rb0.2AgO
...
I want to filter it out based on specified elements. For example I want to produce an output which only contains the elements 'Na','Cl','Rb' therefore the desired output should result in:
formula
Na0.2Cl0.4O0.7Rb1
What I've tried to do is the following
for i, formula in enumerate(df['formula'])
if ('Na' and 'Cl' and 'Rb' not in formula):
df = df.drop(index=i)
but it seems not to work.
You can use use contains with or condition for multiple string pattern matching for matching only one of them
df[df['formula'].str.contains("Na|Cl|Rb", na=False)]
Or you can use pattern with contains if you want to match all of them
df[df['formula'].str.contains(r'^(?=.*Na)(?=.*Cl)(?=.*Rb)')]
Your requirements are unclear, but assuming you want to filter based on a set of elements.
Keeping formulas where all elements from the set are used:
s = {'Na','Cl','Rb'}
regex = f'({"|".join(s)})'
mask = (
df['formula']
.str.extractall(regex)[0]
.groupby(level=0).nunique().eq(len(s))
)
df.loc[mask[mask].index]
output:
formula
0 Na0.2Cl0.4O0.7Rb1
Keeping formulas where only elements from the set are used:
s = {'Na','Cl','Rb'}
mask = (df['formula']
.str.extractall('([A-Z][a-z]*)')[0]
.isin(s)
.groupby(level=0).all()
)
df[mask]
output: no rows for this dataset
Related
I want to compare two columns actual_data and pipeline_data based on source column bcz every source has different format.
I am trying to achieve the result column based on comparision between actual_data and pipeline_data .
I am new to pandas and looking for a way to implement this.
df['result'] = np.where(df['pipeline_data'].str.len() == df['actual_data'].str.len(), 'Match', np.where(df['pipeline_data'].str.len() > df['actual_data'].str.len(), 'Length greater than actual_data', 'Length shorter than actual_data'))
The code above should to what you want to do.
I have a dataframe which has a column called hexa which has hex values like this. They are of dtype object.
hexa
0 00802259AA8D6204
1 00802259AA7F4504
2 00802259AA8D5A04
I would like to remove the first and last bits and reverse the values bitwise as follows:
hexa-rev
0 628DAA592280
1 457FAA592280
2 5A8DAA592280
Please help
I'll show you the complete solution up here and then explain its parts below:
def reverse_bits(bits):
trimmed_bits = bits[2:-2]
list_of_bits = [i+j for i, j in zip(trimmed_bits[::2], trimmed_bits[1::2])]
reversed_bits = [list_of_bits[-i] for i in range(1,len(list_of_bits)+1)]
return ''.join(reversed_bits)
df['hexa-rev'] = df['hexa'].apply(lambda x: reverse_bits(x))
There are possibly a couple ways of doing it, but this way should solve your problem. The general strategy will be defining a function and then using the apply() method to apply it to all values in the column. It should look something like this:
df['hexa-rev'] = df['hexa'].apply(lambda x: reverse_bits(x))
Now we need to define the function we're going to apply to it. Breaking it down into its parts, we strip the first and last bit by indexing. Because of how negative indexes work, this will eliminate the first and last bit, regardless of the size. Your result is a list of characters that we will join together after processing.
def reverse_bits(bits):
trimmed_bits = bits[2:-2]
The second line iterates through the list of characters, matches the first and second character of each bit together, and then concatenates them into a single string representing the bit.
def reverse_bits(bits):
trimmed_bits = bits[2:-2]
list_of_bits = [i+j for i, j in zip(trimmed_bits[::2], trimmed_bits[1::2])]
The second to last line returns the list you just made in reverse order. Lastly, the function returns a single string of bits.
def reverse_bits(bits):
trimmed_bits = bits[2:-2]
list_of_bits = [i+j for i, j in zip(trimmed_bits[::2], trimmed_bits[1::2])]
reversed_bits = [list_of_bits[-i] for i in range(1,len(list_of_bits)+1)]
return ''.join(reversed_bits)
I explained it in reverse order, but you want to define this function that you want applied to your column, and then use the apply() function to make it happen.
I want to take data from one set and enter it into another empty set.
So, for example, I want to do something like:
if ([i,x] > 9){
new_data$House[y,x] <- data[i,2]
}
but I want to do it over and over, creating new rows in new_data.
How do I keep adding data to new_data and overriding/saving the new row?
Essentially, I just want to know how to "grow" an empty data set.
Please ignore any errors in the code, it is just an example and I am still working on other details.
Thanks
If you are using r language, I presume you are looking for rbind:
new_data = NULL # define your new dataset
for(i in 1:nrow(data)) # loop over row of data
{
if(data[i,x] > 9) # if statement for implementing a condition
{
new_data = rbind(new_data,data[i,2:6]) # adding values of the row i and column 2 to 6
}
}
At the end, new_data will contain as many rows that satisfy the if statement and each row will contain values extracted from column 2 to 6.
If it is what you are looking for, there is various ways to do that without the need of a for loop, as an example:
new_data = data[data[i,x]>9,2:6]
If this answer is not satisfying for you, please provide more details in your question, include a reproducible example of your data and the expected output
I'm trying to perform calculations based on the entries in a pandas dataframe. The dataframe looks something like this:
and it contains 1466 rows. I'll have to run similar calculations on other dfs with more rows later.
What I'm trying to do, is calculate something like mag='(U-V)/('R-I)' (but ignoring any values that are -999), put that in a new column, and then z_pred=10**((mag-c)m) in a new column (mag, c and m are just hard-coded variables). I have other columns I need to add too, but I figure that'll just be an extension of the same method.
I started out by trying
for i in range(1):
current = qso[:]
mag = (U-V)/(R-I)
name = current['NED']
z_pred = 10**((mag - c)/m)
z_meas = current['z']
but I got either a Series for z, which I couldn't operate on, or various type errors when I tried to print the values or write them to a file.
I found this question which gave me a start, but I can't see how to apply it to multiple calculations, as in my situation.
How can I achieve this?
Conditionally adding calculated columns row wise are usually performed with numpy's np.where;
df['mag'] = np.where(~df[['U', 'V', 'R', 'I']].eq(-999).any(1), (df.U - df.V) / (df.R - df.I), -999)
Note; assuming here that when any of the columns contain '-999' it will not be calculated and a '-999' is returned.
I'm a beginner to R from a SAS background trying to do a basic "case when" match on two tables to get a flag where I have and have not found a match. Please see the SAS code I have in mind below. I just need something analogous to this in R. Thanks in advance.
proc sql;
create table
x as
select
a.*,
b.*,
case when a.first_column=b.column_first and
a.second_column=b.column_second
then 1 else 0 end as matched_flag
from table1 as a
left join
table2 as b
on a.first_column=b.column_first and a.second_column=b.column_second;
quit;
I'm not familiar with SAS, but I think I understand what you are trying to do. To see how many rows/columns are similar between two tables, you can use %in% and the length function.
For example, initialize two matrices of different dimensions and given them similar row names and column names:
mat.a <- matrix(1, nrow=3, ncol = 2)
mat.b <- matrix(1, nrow=2, ncol = 3)
rownames(mat.a) <- c('a','b','c')
rownames(mat.b) <- c('a','d')
colnames(mat.a) <- c('g','h')
colnames(mat.b) <- c('h','i')
mat.a and mat.b now exist with different row and column names. To match the rows by names, you can use:
row.match <- rownames(mat.a)[rownames(mat.a) %in% rownames(mat.b)]
num.row.match <- length(row.match)
Note that row.match can now be used to index into both of the matrices. The %in% operator returns a logical of the same length of the first argument (in this case, rownames(mat.a)) that indicates if the ith element of the first argument was found anywhere in the elements of the second argument. This nature of %in% means that you have to be sensitive to how you order the arguments for your indexing.
If you simply want to quantify how many rows or columns are the same between the two matrices, then you can use the sum function with the %in% operator:
sum(rownames(mat.a) %in% rownames(mat.b))
With the sum function used like this, you do not need to be sensitive to how you order the arguments, because the number of row names of mat.a in row names of mat.b is equivalent to the number of row names of mat.b in row names of mat.a. That is to say that this usage of %in% is commutative.
I hope this helps!
You will want to use dataframe objects. These are like datasets in SAS. You can use bind to put two dataframe objects together side by side. Then you can select rows based on conditions and set the flag based on this. In the code below you will see that I did this twice: once to set the 1 flag and once to set the 0 flag.
To select the rows where all fields match you can do something similar, but instead of assigning a new column you can assign all the results back to the name of the table you are working on.
Here's the code:
# make up example a and b data frames
table1 <- data.frame(list(a.first_column=c(1,2,3),a.second_column=c(4,5,6)))
table2 <- data.frame(list(b.first_column=c(1,3,6),b.second_column=c(4,5,9)))
# Combine columns (horizontally)
x <- cbind(table1, table2)
print("Combined Data Frames")
print(x)
# create matched flag (1 when the first columns match)
x$matched_flag[x$a.first_column==x$b.first_column] <- 1
x$matched_flag[!x$a.first_column==x$b.first_column] <- 0
# only select records that match both data frames
x <- x[x$a.first_column==x$b.first_column & x$a.second_column==x$b.second_column,]
print("Matched Data Frames")
print(x)
BTW: since you are used to using SQL, you might want to try the sqldf package in R. It will let you use the same techniques that you are used to but in R and on data frames.