I tried to apply pandas get_dummies function to my dataset.
The problem is category value's number is not matched train set and valid set.
For example, train set column has 5 kind of values. ex : [1, 2, 3, 4, 5]
However, valid set has just 3 kind of values. ex : [1, 3, 5]
When I made model by using train dataset there were 5 dummies is being created.
ex: dum_1, dum_2, dum_3, dum_4, dum_5
So, if i just used same function for valid data set this will be made only 3 dummies will be created.
ex: dum_1, dum_2, dum_3
It is not possible to predict valid data set to use my model.
How to make same dummies for train and valid set?
(It is not possible to concat 2 dataset. Please suggest another method except using pd.concat)
Also, if I add new column for valid set, I expect it will make different result.
because dummies sequence is not matching between train and valid set.
thanks.
All you need to do is
Create columns in the validation dataset which are present in the training data but missing in the validation data.
missing_cols = [col for col in train.columns if col not in valid.columns]
for col in missing_cols:
valid[col] = 0
Now, these columns are created in the end, so the order of the columns would be changed. Thus in the next step we would rearrange the columns as below:
valid = valid[[train.columns]]
I am trying to assign the values from a single row in a DataFrame to multiple rows. I have a DF def_security where the first row looks like this (the column headers are AGG and SPY, and the row index is the date)
AGG SPY
2006-01-01 95 21
The rest of the DF all have zeros.
AGG SPY
2006-01-02 0 0
...........
I would like to assign the same value as the first row (the values are calculated and not assigned scalars) to the next 250 rows of def_security. The column headers are user-input and the number of columns or the column headers are not pre-defined. However, there are same number of columns in each row
I am trying with the code
def_security.iloc[1:251] = def_security.iloc[0]
but it is returning error msg "could not broadcast input array from shape(250) into shape (250,2)".
What is the easiest way to do this ?
You were nearly right :-)
Try this:
def_security.iloc[1:251] = def_security.iloc[0].values
I assume this code would work:
def_security.iloc[1:251,-2]=def_security.iloc[0].at['AGG']
def_security.iloc[1:251,-1]=def_security.iloc[0].at['SPY']
First explaining the dataframe, the values of columns '0-156', '156-234', '234-546' .... '> 76830' is the percentage distribution for each range of distances in meters, totaling 100%.
Column 'Cell Name' refers to the data element of the other columns and the column 'Distance' is the column that will trigger the desired sum.
I need to sum the values of the columns '0-156', '156-234', '234-546' .... '> 76830' which are less than the value of the 'Distance' (Meters) column.
Below creation code for testing.
import pandas as pd
# initialize list of lists
data = [['Test1',0.36516562,19.065996,49.15094,24.344206,0.49186087,1.24217,5.2812457,0.05841639,0,0,0,0,158.4122868],
['Test2',0.20406325,10.664485,48.70978,14.885571,0.46103176,8.75815,14.200708,2.1162114,0,0,0,0,192.553074],
['Test3',0.13483211,0.6521175,6.124511,41.61725,45.0036,5.405257,1.0494527,0.012979688,0,0,0,0,1759.480042]
]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['Cell Name','0-156','156-234','234-546','546-1014','1014-1950','1950-3510','3510-6630','6630-14430','14430-30030','30030-53430','53430-76830','>76830','Distance'])
Example of what should be done:
The value of column 'Distance' = 158.412286772863 therefore would have to sum the values <= of the following columns, 0-156, '156-234' totalizing 19.43116162 %.
Thanks so much!
As I understand it, you want to sum up all the percentage values in a row, where the lower value of the column-description (in case of '0-156' it would be 0, in case of '156-234' it would be 156, and so on...) is smaller than the value in the distance column.
First I would suggest, that you transform your string-like column-names into values, as an example:
lowerlimit=df.columns[2]
>>'156-234'
Then read the string only till the '-' and make it a number
int(lowerlimit[:lowerlimit.find('-')])
>> 156
You can loop this through all your columns and make a new row for the lower limits.
For a bit more simplicity I left out the first column for your example, and added another first row with the lower limits of each column, that you could generate as described above. Then this code works:
data = [[0,156,234,546,1014,1950,3510,6630,11430,30030,53430,76830,1e-23],[0.36516562,19.065996,49.15094,24.344206,0.49186087,1.24217,5.2812457,0.05841639,0,0,0,0,158.4122868],
[0.20406325,10.664485,48.70978,14.885571,0.46103176,8.75815,14.200708,2.1162114,0,0,0,0,192.553074],
[0.13483211,0.6521175,6.124511,41.61725,45.0036,5.405257,1.0494527,0.012979688,0,0,0,0,1759.480042]
]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['0-156','156-234','234-546','546-1014','1014-1950','1950-3510','3510-6630','6630-14430','14430-30030','30030-53430','53430-76830','76830-','Distance'])
df['lastindex']=None
df['sum']=None
After creating basically your dataframe, I add two columns 'lastindex' and 'sum'.
Then I am searching for the last index in every row, that is has its lower limit below the distance given in that row (df.iloc[x,-3]); afterwards I'm summing up the respective columns in that row.
for i in np.arange(1,len(df)):
df.at[i,'lastindex']=np.where(df.iloc[0,:-3]<df.iloc[i,-3])[0][-1]
df.at[i,'sum']=sum(df.iloc[i][0:df.at[i,'lastindex']+1])
I hope, this is helpful. Best, lepakk
What is the meaning of below lines., especially confused about how iloc[:,1:] is working ? and also data[:,:1]
data = np.asarray(train_df_mv_norm.iloc[:,1:])
X, Y = data[:,1:],data[:,:1]
Here train_df_mv_norm is a dataframe --
Definition: pandas iloc
.iloc[] is primarily integer position based (from 0 to length-1 of the
axis), but may also be used with a boolean array.
For example:
df.iloc[:3] # slice your object, i.e. first three rows of your dataframe
df.iloc[0:3] # same
df.iloc[0, 1] # index both axis. Select the element from the first row, second column.
df.iloc[:, 0:5] # first five columns of data frame with all rows
So, your dataframe train_df_mv_norm.iloc[:,1:] will select all rows but your first column will be excluded.
Note that:
df.iloc[:,:1] select all rows and columns from 0 (included) to 1 (excluded).
df.iloc[:,1:] select all rows and columns, but exclude column 1.
To complete the answer by KeyMaker00, I add that data[:,:1] means:
The first : - take all rows.
:1 - equal to 0:1 take columns starting from column 0,
up to (excluding) column 1.
So, to sum up, the second expression reads only the first column from data.
As your expression has the form:
<variable_list> = <expression_list>
each expression is substituted under the corresponding variable (X and Y).
Maybe it will complete the answers before.
You will know
what you get,
its shape
how to use it with de column name
df.iloc[:,1:2] # get column 1 as a DATAFRAME of shape (n, 1)
df.iloc[:,1:2].values # get column 1 as an NDARRAY of shape (n, 1)
df.iloc[:,1].values # get column 1 as an NDARRAY of shape ( n,)
df.iloc[:,1] # get column 1 as a SERIES of shape (n,)
# iloc with the name of a column
df.iloc[:, df.columns.get_loc('my_col')] # maybe there is some more
elegants methods
In my training set I have 24 Feature Vectors(FV). Each FV contains 2 lists. When I try to fit this on model = LogisticRegression() or model = KNeighborsClassifier(n_neighbors=k) I get this error ValueError: setting an array element with a sequence.
In my dataframe, each row represents each FV. There are 3 columns. The first column contains a list of an individual's heart rate, second a list of the corresponding activity data and third the target. Visually, it looks like something like this:
HR ACT Target
[0.5018, 0.5106, 0.4872] [0.1390, 0.1709, 0.0886] 1
[0.4931, 0.5171, 0.5514] [0.2423, 0.2795, 0.2232] 0
Should I:
Join both lists to form on long FV
Expand both lists such that each column represents one value. In other words, if there are 5 items in HR and ACT data for a FV, the new dataframe would have 10 columns for features and 1 for Target.
How does Logistic Regression and KNNs handle input data? I understand that logistic regression combines the input linearly using weights or coefficient values. But I am not sure what that means when it comes to lists VS dataframe columns. Does it mean it automatically converts corresponding values of dataframe columns to a list before transforming? Is there a difference between method 1 and 2?
Additionally, if a long list is required, should I have the long list as [HR,HR,HR,ACT,ACT,ACT] or [HR,ACT,HR,ACT,HR,ACT].
You should go with 2
Expand both lists such that each column represents one value. In other words, if there are 5 items in HR and ACT data for a FV, the new dataframe would have 10 columns for features and 1 for Target.
You should then select the feature columns from the dataframe and pass it as X, and the target column as Y to the model's fit function.
Sklearn's models accepts inputs with the following shape [n_samples, n_features], and since after following the 2nd solution you proposed, your training dataframe will have 2D of the shape [n_samples, 10].