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.
Related
Hello,
I am analyzing the next dataset with this information .
The column ['program_number'] is an object but I want to change it to a integer colum.
I have tried to replace some values but it doesn´t work.
as you can see, some values like 6 is duplicate. like '6 ' and 6.
How can I resolve it? Many thanks
UPDATE
Didn't see 1X and 3X at first.
If you need those numbers and just want to remove the X then:
df["Program"] = df["Program"].str.strip(" X").astype(int)
If there is data in the column which aren't numbers or which shouldn't be converted, you can use pd.to_numeric with errors='corece'. If there are cells which can't be converted, you'll get NaN. Be aware that this will result in floating numbers.
df["Program"] = pd.to_numeric(df["Program"], errors="coerce")
old
You want to use str.strip() here, rather than replace.
Try this:
df1['program_number'] = df1['program_number'].str.strip().astype(int)
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
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 have just tried my first sqlite select-statement and got a result (an iterator over tuples). So, in other words, every row is represented by a tuple and I can access value in the cells of the row like this: r[7] or r[3] (get value from the column 7 or column 3). But I would like to access columns not by their positions but by their names. Let us say, I would like to know the value in the column user_name. What is the way to do it?
I found the answer on my question here:
cursor.execute("PRAGMA table_info(tablename)")
print cursor.fetchall()