This question already has answers here:
select columns based on columns names containing a specific string in pandas
(4 answers)
Closed 4 years ago.
Due to large number of columns I was trying to search for column names where the column name has 'el' in it. This is the code I tried
combined.columns.str.contains("el")
But the result I'm getting is a Boolean array how can I view the column names that returned true from it.
Use boolean indexing with loc with : for select all rows:
combined.loc[:, combined.columns.str.contains("el")]
Or use DataFrame.filter:
combined.filter(like='el')
Related
This question already has answers here:
How can I pivot a dataframe?
(5 answers)
How to pivot a dataframe in Pandas? [duplicate]
(2 answers)
Closed 1 year ago.
I have a dataframe like this:
index,col1,value
1,A,1
1,B,2
2,A,3
2,D,4
2,C,5
2,B,6
And I would like to convert this dataframe to this:
index,col1_A,col1_B,col1_C,col1_D
1,1,2,np.Nan,np.nan
2,3,4,5,6
The conversion is based on the index column value and for each unique index column, the column values from col1 is converted to column name and its associated value is set to the corresponding value available in value columns.
Currently my solution contains looping by creating subset of df as temporary df based on each index and then starting looping there. I am wondering if there is already builtin solution available for it in pandas. please feel free to suggest.
This question already has answers here:
Pandas fill missing values in dataframe from another dataframe
(6 answers)
Closed 1 year ago.
I am trying to replace the values of my second dataframe ('area') with those of my first dataframe ('test').
Image of my inputs:
The catch is that I only want to replace the values that are not NaN, so, for example, area.iloc[0,1] will be "6643.68" rather than "3321.84" but area.iloc[-2,-1] will be "19.66" rather than "NaN". I would have thought I could do something like:
area.loc[test.notnull()] = test
or
area.replace(area.loc[test.notnull()], test.notnull())
But this gives me the error "Cannot index with multidimensional key". Any ideas? This should be simple.
Use fillna like:
area.fillna(test)
This question already has answers here:
Remove duplicates from dataframe, based on two columns A,B, keeping row with max value in another column C
(4 answers)
how do I remove rows with duplicate values of columns in pandas data frame?
(4 answers)
Drop all duplicate rows across multiple columns in Python Pandas
(8 answers)
Closed 2 years ago.
I have an Excel file that contains the following information
and I need to extract that data into a dataframe with unique values from column ID and value 1; so that I will end up with something like this:
I have done the following:
df=pd.read_excel(file)
dfUnique=pd.unique(df[["column ID","value1"]]).values.ravel('K')
but I get the following error:
could not broadcast input array from shape (5,2) into shape (5)
This question already has answers here:
How do I select rows from a DataFrame based on column values?
(16 answers)
Closed 3 years ago.
I'm trying to access all the rows of a dataframe if the "match" column is "False".
Since I can't upload an image, I will explain the data. The dataframe has several columns, and I am only interested in the "match" column. I will keep all the rows when the "match" column's values are "False"
SyntaxError: invalid syntax
You don't need to do this, this is what boolean filtering by a logical expression is for:
df23[df23['match'] == False]
or to be more concise:
df23[~ df23['match']]
This question already has answers here:
Filter dataframe rows if value in column is in a set list of values [duplicate]
(7 answers)
Closed 4 years ago.
I have a pandas dataframe that looks like this:
From this, I want to grab all the rows for particular Filters (1st column). So for example, I want to grab the rows for F218W, F336W, and F373N.
What is the easiest way to do this in pandas?
In addition if I wanted to grab the rows for those filters but also only for Chip 1, how could I do that easily?
Thanks!
This is a simple slicing:
df[df["# Filter"].isin(["F218W", "F336W","F373N"])]
If the rules across multiple columns, you can simply combine them using &:
df[df["# Filter"].isin(["F218W", "F336W","F373N"]) & (df["Chip"] == 1)]