I have a data frame, where I would like to merge the content of two rows, and have it separated by underscore, within the same cell.
If this is the original DF:
0 eye-right eye-right hand
1 location location position
2 12 27.7 2
3 14 27.6 2.2
I would like it to become:
0 eye-right_location eye-right_location hand_position
1 12 27.7 2
2 14 27.6 2.2
Eventually I would like to translate row 0 to become header, and reset indexes for the entire df.
You can set your column labels, slice via iloc, then reset_index:
print(df)
# 0 1 2
# 0 eye-right eye-right hand
# 1 location location position
# 2 12 27.7 2
# 3 14 27.6 2.2
df.columns = (df.iloc[0] + '_' + df.iloc[1])
df = df.iloc[2:].reset_index(drop=True)
print(df)
# eye-right_location eye-right_location hand_position
# 0 12 27.7 2
# 1 14 27.6 2.2
I like jpp's answer a lot. Short and sweet. Perfect for quick analysis.
Just one quibble: The resulting DataFrame is generically typed. Because strings were in the first two rows, all columns are considered type object. You can see this with the info method.
For data analysis, it's often preferable that columns have specific numeric types. This can be tidied up with one more line:
df.columns = df.iloc[0] + '_' + df.iloc[1]
df = df.iloc[2:].reset_index(drop=True)
df = df.apply(pd.to_numeric)
The third line here applies Panda's to_numeric function to each column in turn, leaving a more-typed DataFrame:
While not essential for simple usage, as soon as you start performing math on DataFrames, or start using very large data sets, column types become something you'll need to pay attention to.
Related
I have this dataframe and my goal is to remove any columns that have less than 1000 entries.
Prior to to pivoting the df I know I have 880 unique well_id's with entries ranging from 4 to 60k+. I know should end up with 102 well_id's.
I tried to accomplish this in a very naïve way by collecting the wells that I am trying to remove in an array and using a loop but I keep getting a 'TypeError: Level type mismatch' but when I just use del without a for loop it works.
#this works
del df[164301.0]
del df['TB-0071']
# this doesn't work
for id in unwanted_id:
del df[id]
Any help is appreciated, Thanks.
You can use dropna method:
df.dropna(thresh=[]) #specify [here] how many non-na values you require to keep the row
The advantage of this method is that you don't need to create a list.
Also don't forget to add the usual inplace = True if you want the changes to be made in place.
You can use pandas drop method:
df.drop(columns=['colName'], inplace=True)
You can actually pass a list of columns names:
unwanted_id = [164301.0, 'TB-0071']
df.drop(columns=unwanted_ids, inplace=True)
Sample:
df[:5]
from to freq
0 A X 20
1 B Z 9
2 A Y 2
3 A Z 5
4 A X 8
df.drop(columns=['from', 'to'])
freq
0 20
1 9
2 2
3 5
4 8
And to get those column names with more than 1000 unique values, you can use something like this:
counts = df.nunique()[df.nunique()>1000].to_frame('uCounts').reset_index().rename(columns={'index':'colName'})
counts
colName uCounts
0 to 1001
1 freq 1050
I want to add an aggregate, grouped, nunique column to my pandas dataframe but not aggregate the entire dataframe. I'm trying to do this in one line and avoid creating a new aggregated object and merging that, etc.
my df has track, type, and id. I want the number of unique ids for each track/type combination as a new column in the table (but not collapse track/type combos in the resulting df). Same number of rows, 1 more column.
something like this isn't working:
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].nunique()
nor is
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(nunique)
this last one works with some aggregating functions but not others. the following works (but is meaningless on my dataset):
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(sum)
in R this is easily done in data.table with
df[, n_unique_id := uniqueN(id), by = c('track', 'type')]
thanks!
df.groupby(['track', 'type'])['id'].transform(nunique)
Implies that there is a name nunique in the name space that performs some function. transform will take a function or a string that it knows a function for. nunique is definitely one of those strings.
As pointed out by #root, often the method that pandas will utilize to perform a transformation indicated by these strings are optimized and should generally be preferred to passing your own functions. This is True even for passing numpy functions in some cases.
For example transform('sum') should be preferred over transform(sum).
Try this instead
df.groupby(['track', 'type'])['id'].transform('nunique')
demo
df = pd.DataFrame(dict(
track=list('11112222'), type=list('AAAABBBB'), id=list('XXYZWWWW')))
print(df)
id track type
0 X 1 A
1 X 1 A
2 Y 1 A
3 Z 1 A
4 W 2 B
5 W 2 B
6 W 2 B
7 W 2 B
df.groupby(['track', 'type'])['id'].transform('nunique')
0 3
1 3
2 3
3 3
4 1
5 1
6 1
7 1
Name: id, dtype: int64
I have a file with 1M rows that I'm trying to read into 20 DataFrames. I do not know in advance which row belongs to which DataFrame or how large each DataFrame will be. How can I process this file into DataFrames efficiently? I've tried to do this several different ways. Here is what I currently have:
data = pd.read_csv(r'train.data', sep=" ", header = None) # Not slow
def collectData(row):
id = row[0]
df = dictionary[id] # Row content determines which dataframe this row belongs to
next = len(df.index)
df.loc[next] = row
data.apply(collectData, axis=1)
It's very slow. What am I doing wrong? If I just apply an empty function, my code runs in 30 sec. With the actual function it takes at least 10 minutes and I'm not sure if it would finish.
Here are a few sample rows from the dataset:
1 1 4
1 2 2
1 3 10
1 4 4
The full dataset is available here (if you click on Matlab version)
Your approach is not a vectored one, because you apply a python function row by row.
Rather that creating 20 dataframes , make a dictionary containing an index (in range(20)) for each key[0]. Then add this information to your DataFrame:
data['dict']=data[0].map(dictionary)
Then reorganize :
data2=data.reset_index().set_index(['dict','index'])
data2 is like :
0 1 2
dict index
12 0 1 1 4
1 1 2 2
2 1 3 10
3 1 4 4
4 1 5 2
....
and data2.loc[i] is one of the Dataframe you want.
EDIT:
It seems that dictionary is describe in train.label.
You can set the dictionary before by:
with open(r'train.label') as f: u=f.readlines()
v=[int(x) for x in u] # len(v) = 11269 = data[0].max()
dictionary=dict(zip(range(1,len(v)+1),v))
Since, the full data set is easily loaded into memory, the following should be fairly quick
data_split = {i: data[data[0] == i] for i in range(1, 21)}
# to access each dataframe, do a dictionary lookup, i.e.
data_split[2].head()
0 1 2
769 2 12 4
770 2 16 2
771 2 23 4
772 2 27 2
773 2 29 6
you may also want to reset the indices or copy the data frame when you're slicing the data frame into smaller data frames.
additional reading:
copy
reset_index
view-vs-copy
If you want to build them efficiently, I think you need some good raw materials:
wood
cement
Are robust and durable.
Try to avoid using hay as the dataframe can be blown up with a little wind.
Hope that helps
I'm new to Python, and I believe this is very basic question (sorry for that), but I tried to look for a solution here: Better way to add constant column to pandas data frame and here: add column with constant value to pandas dataframe and in many other places...
I have a data frame like this "toy" sample:
A B
10 5
20 12
50 200
and I want to add new column (C) which will be the division of the last data cells of A and B (50/200); So in my example, I'd like to get:
A B C
10 5 0.25
20 12 0.25
50 200 0.25
I tried to use this code:
groupedAC ['pNr'] = groupedAC['cIndCM'][-1:]/groupedAC['nTileCM'][-1:]
but I'm getting the result only in the last cell (I believe it's a result of my code acting as a "pointer" and not as a number - but as I said, I tried to "convert" my result into a constant (even using temp variables) but with no success).
Your help will be appreciated!
You need to index it with .iloc[-1] instead of .iloc[-1:], because the latter returns a Series and thus when assigning back to the data frame, the index needs to be matched:
df.B.iloc[-1:] # return a Series
#2 150
#Name: B, dtype: int64
df['C'] = df.A.iloc[-1:]/df.B.iloc[-1:] # the index has to be matched in this case, so only
# the row with index = 2 gets updated
df
# A B C
#0 10 5 NaN
#1 20 12 NaN
#2 50 200 0.25
df.B.iloc[-1] # returns a constant
# 150
df['C'] = df.A.iloc[-1]/df.B.iloc[-1] # there's nothing to match when assigning the
# constant to a new column, the value gets broadcasted
df
# A B C
#0 10 5 0.25
#1 20 12 0.25
#2 50 200 0.25
I have a problem with adding columns in pandas.
I have DataFrame, dimensional is nxk. And in process I wiil need add columns with dimensional mx1, where m = [1,n], but I don't know m.
When I try do it:
df['Name column'] = data
# type(data) = list
result:
AssertionError: Length of values does not match length of index
Can I add columns with different length?
If you use accepted answer, you'll lose your column names, as shown in the accepted answer example, and described in the documentation (emphasis added):
The resulting axis will be labeled 0, ..., n - 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information.
It looks like column names ('Name column') are meaningful to the Original Poster / Original Question.
To save column names, use pandas.concat, but don't ignore_index (default value of ignore_index is false; so you can omit that argument altogether). Continue to use axis=1:
import pandas
# Note these columns have 3 rows of values:
original = pandas.DataFrame({
'Age':[10, 12, 13],
'Gender':['M','F','F']
})
# Note this column has 4 rows of values:
additional = pandas.DataFrame({
'Name': ['Nate A', 'Jessie A', 'Daniel H', 'John D']
})
new = pandas.concat([original, additional], axis=1)
# Identical:
# new = pandas.concat([original, additional], ignore_index=False, axis=1)
print(new.head())
# Age Gender Name
#0 10 M Nate A
#1 12 F Jessie A
#2 13 F Daniel H
#3 NaN NaN John D
Notice how John D does not have an Age or a Gender.
Use concat and pass axis=1 and ignore_index=True:
In [38]:
import numpy as np
df = pd.DataFrame({'a':np.arange(5)})
df1 = pd.DataFrame({'b':np.arange(4)})
print(df1)
df
b
0 0
1 1
2 2
3 3
Out[38]:
a
0 0
1 1
2 2
3 3
4 4
In [39]:
pd.concat([df,df1], ignore_index=True, axis=1)
Out[39]:
0 1
0 0 0
1 1 1
2 2 2
3 3 3
4 4 NaN
We can add the different size of list values to DataFrame.
Example
a = [0,1,2,3]
b = [0,1,2,3,4,5,6,7,8,9]
c = [0,1]
Find the Length of all list
la,lb,lc = len(a),len(b),len(c)
# now find the max
max_len = max(la,lb,lc)
Resize all according to the determined max length (not in this example
if not max_len == la:
a.extend(['']*(max_len-la))
if not max_len == lb:
b.extend(['']*(max_len-lb))
if not max_len == lc:
c.extend(['']*(max_len-lc))
Now the all list is same length and create dataframe
pd.DataFrame({'A':a,'B':b,'C':c})
Final Output is
A B C
0 1 0 1
1 2 1
2 3 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
I had the same issue, two different dataframes and without a common column. I just needed to put them beside each other in a csv file.
Merge:
In this case, "merge" does not work; even adding a temporary column to both dfs and then dropping it. Because this method makes both dfs with the same length. Hence, it repeats the rows of the shorter dataframe to match the longer dataframe's length.
Concat:
The idea of The Red Pea didn't work for me. It just appended the shorter df to the longer one (row-wise) while leaving an empty column (NaNs) above the shorter df's column.
Solution: You need to do the following:
df1 = df1.reset_index()
df2 = df2.reset_index()
df = [df1, df2]
df_final = pd.concat(df, axis=1)
df_final.to_csv(filename, index=False)
This way, you'll see your dfs besides each other (column-wise), each of which with its own length.
If somebody like to replace a specific column of a different size instead of adding it.
Based on this answer, I use a dict as an intermediate type.
Create Pandas Dataframe with different sized columns
If the column to be inserted is not a list but already a dict, the respective line can be omitted.
def fill_column(dataframe: pd.DataFrame, list: list, column: str):
dict_from_list = dict(enumerate(list)) # create enumertable object from list and create dict
dataFrame_asDict = dataframe.to_dict() # Get DataFrame as Dict
dataFrame_asDict[column] = dict_from_list # Assign specific column
return pd.DataFrame.from_dict(dataFrame_asDict, orient='index').T # Create new DataSheet from Dict and return it