Develop Reference

add/combine columns after searching in a DataFrame

I'm trying to copy data from different columns to a particular column in the same DataFrame.
Index
col1A
col2A
colB
list
CT
CW
CH
0
1
:
1
b
2
2
3
3d
But prior to that I wanted to search if those columns(col1A,col2A,colB) exist in the DataFrame and group those columns which are present and move the grouped data to relevant columns(CT,CH,etc) like,
CH
CW
CT
0
1
1
1
b
b
2
2
2
3
3d
3d
I did,
col_list1 = ['ColA','ColB','ColC']
test1 = any([ i in df.columns for i in col_list1 ])
if test1==True:
df['CH'] = df['Col1A'] +df['Col2A']
df['CT'] = df['ColB']
this code is throwing me a keyerror
.
I want it to ignore columns that are not present and add only those that are present

IIUC, you can use Python set or Series.isin to find the common columns
cols = list(set(col_list1) & set(df.columns))
# or
cols = df.columns[df.columns.isin(col_list1)]
df['CH'] = df[cols].sum(axis=1)

Instead of just concatenating the columns with +, collect them into a list and use sum with axis=1:
df['CH'] = np.sum([df[c] for c in cl if c in df], axis=1)

Appending two dataframes with multindex rows?

I have two dataframes:
The first one looks like this:
variable
entry
subentry
0
1
X
2
Y
3
Z
and the second one looks like:
variable
entry
subentry
0
1
A
2
B
I would like to merge the two dataframe such that I get:
variable
entry
subentry
0
1
X
2
Y
3
Z
1
1
A
2
B
Simply using df1.append(df2, ignore_index=True) gives
variable
0
X
1
Y
2
Z
3
A
4
B
In other words, it collapses the multindex into a single index. Is there a way around this?
Edit: Here is a code sinppet that will reproduce the problem:
arrays = [
np.array([0,0,0]),
np.array([0,1,2]),]
arrays_2 = [
np.array([0,0]),
np.array([0,1]),]
df1 = pd.DataFrame(np.random.randn(3, 1), index=arrays)
df2 = pd.DataFrame(np.random.randn(2, 1), index=arrays_2)
df = df1.append(df2, ignore_index=True)
print(df)
Edit: In practice, I am looking ao combine N dataframes, each with a different number of "entry" rows. So I am looking for an approach that will not rely on me knowing the exact of the dataframes I am combining.

One way try:
pd.concat([df1, df2], keys=[0,1]).droplevel(1)
Output:
0
0 0 -0.439749
1 -0.478744
2 0.719870
1 0 -1.055648
1 -2.007242
Use pd.concat to concat the dataframes together and since entry is the same of both, use keys parameter to create a new level with the naming you want your level to be. Finally, go back and drop the old index level (where the value was the same).

Add multi level column to dataframe

At the beginning, I'd like to add a multilevel column to an empty dataframe.
df = pd.DataFrame({"nodes": list(range(1, 5, 2))})
df.set_index("nodes", inplace=True)
So this is the dataframe to start with (still empty):
>>> df
nodes
1
3
Now I'd like to a first multilevel column.
I tried the following:
new_df = pd.DataFrame.from_dict(dict(zip(df.index, [1,2])), orient="index",
columns=["value"])
df = pd.concat([new_df], axis=1, keys=["test"])
Now the dataframe df looks like this:
>>> df
test
value
1 1
3 2
To add another column, i've done something similar.
new_df2 = pd.DataFrame.from_dict(dict(zip(df.index, [3,4])), orient="index",
columns=[("test2", "value2")])
df = pd.concat([df, new_df2], axis=1)
df.index.name = "nodes"
So the desired dataframe looks like this:
>>> df
test test2
nodes value value2
1 1 3
3 2 4
This way of adding multilevel columns seems a bit strange. Is there a better way of doing so?

Create a MultIndex on the columns by storing your DataFrames in a dict then concat along axis=1. The keys of the dict become levels of the column MultiIndex (if you use tuples it adds multiple levels depending on the length, scalar keys add a single level) and the DataFrame columns stay as is. Alignment is enforced on the row Index.
import pandas as pd
d = {}
d[('foo', 'bar')] = pd.DataFrame({'val': [1,2,3]}).rename_axis(index='nodes')
d[('foo2', 'bar2')] = pd.DataFrame({'val2': [4,5,6]}).rename_axis(index='nodes')
d[('foo2', 'bar1')] = pd.DataFrame({'val2': [7,8,9]}).rename_axis(index='nodes')
pd.concat(d, axis=1)
foo foo2
bar bar2 bar1
val val2 val2
nodes
0 1 4 7
1 2 5 8
2 3 6 9

How to add a MultiIndex after loading csv data into a pandas dataframe?

I am trying to add additional index rows to an existing pandas dataframe after loading csv data into it.
So let's say I load my data like this:
columns = ['Relative_Pressure','Volume_STP']
df = pd.read_csv(StringIO(contents), skiprows=4, delim_whitespace=True,index_col=False,header=None)
df.columns = columns
where contents is a string in csv format. The resulting DataFrame might look something like this:
For clarity reasons I would now like to add additional index rows to the DataFrame as shown here:
However in the link these multiple index rows are generated right when the DataFrame is created. I would like to add e.g. rows for unit or descr to the columns.
How could I do this?

You can create a MultiIndex on the columns by specifically creating the index and then assigning it to the columns separately from reading in the data.
I'll use the example from the link you provided. The first method is to create the MultiIndex when you make the dataframe:
df = pd.DataFrame({('A',1,'desc A'):[1,2,3],('B',2,'desc B'):[4,5,6]})
df.columns.names=['NAME','LENGTH','DESCRIPTION']
df
NAME A B
LENGTH 1 2
DESCRIPTION desc A desc B
0 1 4
1 2 5
2 3 6
As stated, this is not what you are after. Instead, you can make the dataframe (from your file for example) and then make the MultiIndex from a set of lists and then assign it to the columns:
df = pd.DataFrame({'desc A':[1,2,3], 'desc B':[4,5,6]})
# Output
desc A desc B
0 1 4
1 2 5
2 3 6
# Create a multiindex from lists
index = pd.MultiIndex.from_arrays((['A', 'B'], [1, 2], ['desc A', 'desc B']))
# Assign to the columns
df.columns = index
# Output
A B
1 2
desc A desc B
0 1 4
1 2 5
2 3 6
# Name the columns
df.columns.names = ['NAME','LENGTH','DESCRIPTION']
# Output
NAME A B
LENGTH 1 2
DESCRIPTION desc A desc B
0 1 4
1 2 5
2 3 6
There are other ways to construct a MultiIndex, for example, from_tuples and from_product. You can read more about Multi Indexes in the documentation.

Added column to existing dataframe but entered all numbers as NaN

So I created two dataframes from existing CSV files, both consisting of entirely numbers. The second dataframe consists of an index from 0 to 8783 and one column of numbers and I want to add it on as a new column to the first dataframe which has an index consisting of a month, day and hour. I tried using append, merge and concat and none worked and then tried simply using:
x1GBaverage['Power'] = x2_cut
where x1GBaverage is the first dataframe and x2_cut is the second. When I did this it added x2_cut on properly but all the values were entered as NaN instead of the numerical values that they should be. How should I be approaching this?

x1GBaverage['Power'] = x2_cut.values
problem solved :)
The thing about pandas is that values are implicitly linked to their indices unless you deliberately specify that you only need the values to be transferred over.

If they're the same row counts and you just want to tack it on the end, the indexes either need to match, or you need to just pass the underlying values. In the example below, columns 3 and 5 are the index matching & value versions, and 4 is what you're running into now:
In [58]: df = pd.DataFrame(np.random.random((3,3)))
In [59]: df
Out[59]:
0 1 2
0 0.670812 0.500688 0.136661
1 0.185841 0.239175 0.542369
2 0.351280 0.451193 0.436108
In [61]: df2 = pd.DataFrame(np.random.random((3,1)))
In [62]: df2
Out[62]:
0
0 0.638216
1 0.477159
2 0.205981
In [64]: df[3] = df2
In [66]: df.index = ['a', 'b', 'c']
In [68]: df[4] = df2
In [70]: df[5] = df2.values
In [71]: df
Out[71]:
0 1 2 3 4 5
a 0.670812 0.500688 0.136661 0.638216 NaN 0.638216
b 0.185841 0.239175 0.542369 0.477159 NaN 0.477159
c 0.351280 0.451193 0.436108 0.205981 NaN 0.205981
If the row counts differ, you'll need to use df.merge and let it know which columns it should be using to join the two frames.