How to get the previous value of the same row (previous column) from the pandas dataframe?

pandas iterrows previous row
pandas shift
pandas copy value from previous row
pandas compare row to previous row
pandas time difference between rows
pandas previous row condition
pandas shift column by 1
pandas diff two dataframes

I want to fetch the value from the previous column but the same row and I need to multiply that value with 5 and write it to the current place.

I have tried shift method of pandas but it's not working. after that, I have written the separate function to get the previous column name..but I think that's not the good approach.

'''
def get_previous_column_name(wkName):
        v = int(wkName.strip('W'))
        newv = str(v - 1)
        if len(newv) == 1:
            newv = '0' + newv
        return 'W' + newv
'''

dataframe: 

W01,W02,W03,W04,W05
7, 8
10,20
20, 40

expected result: 

W01,W02,W03,W04,W05
7, 8, 40, 200, 1000
10, 20, 100, 500, 2500
20, 40, 200, 1000, 5000

Here is one way ffill +cumsum

df=df.ffill(1)*(5)**df.isnull().cumsum(1)
df
Out[230]: 
    W01   W02    W03     W04     W05
0   7.0   8.0   40.0   200.0  1000.0
1  10.0  20.0  100.0   500.0  2500.0
2  20.0  40.0  200.0  1000.0  5000.0

Is there a way in Pandas to use previous row value in dataframe , I have attempted an apply and a shift using an if else by this gives a key error. DataFrame(lst, columns=cols, index=range(5)) df A 0 100 1 101 2 102 3 103 4 104. You can reference the previous row with shift: df['Change'] = df.A - df.A.shift( 1) df 3.6.0, Pandas 0.19.2, NumPy 1.11.3, Numba 0.30.1 # calculator() is same as� For each symbol I want to populate the last column with a value that complies with the following rules: Each buy order (side=BUY) in a series has the value zero (0). For each consecutive buy order the value is increased by one (1). When a sell order (side=SELL) is reached it marks a new buy order serie. Rows with status EXPIRED are skipped

import pandas as pd
data = pd.read_csv('C:/d1', sep=',', header=None,names=['W1','W2'])
df=pd.DataFrame(data)
dfNew=pd.DataFrame(columns=['W1','W2','W3','W4','W5'])
(rows,columns)=df.shape

for index in range(rows):
    tempRow=[df.iat[index,0],df.iat[index,1],df.iat[index,1]*5,df.iat[index,1]*25,df.iat[index,1]*125]
    dfNew.loc[len(dfNew)]=tempRow

print()
print(dfNew)

Pandas DataFrame use previous row value for complicated 'if , you can use .shift() function for accessing previous or next values: previous value for col column: df['col'].shift(). next value for col column: df['col'].shift(-1). If you want to shift your columns without re-writing the whole dataframe or you want to subtract the column value with the previous row value or if you want to find the cumulative sum without using cumsum() function or you want to shift the time index of your dataframe by Hour, Day, Week, Month or Year then to achieve all these tasks you can use pandas dataframe shift function. Here are the

If you indeed have only three columns to fill, just do the multiplication:

df['W03'] = df['W02'] * 5
df['W04'] = df['W03'] * 5
df['W05'] = df['W04'] * 5
df
#   W01  W02  W03   W04   W05
#0    7    8   40   200  1000
#1   10   20  100   500  2500
#2   20   40  200  1000  5000

pandas.DataFrame.diff — pandas 1.1.0 documentation, DataFrame.get � pandas. element compared with another element in the Dataframe (default is element in previous row). Periods to shift for calculating difference, accepts negative values. axis{0 or 'index', 1 or 'columns'}, default 0. I know with .shift() I can refer to a previous cell if they are not in the same column, .rolling() is providing good, but limited options. Also I cannot use cumulative functions, cause the result will differ depending on other columns' output.

Indexing and selecting data — pandas 0.8.1 documentation, previous | next | modules | modules | index As mentioned when introducing the data structures in the last section, the primary function of indexing You may select rows from a DataFrame using a boolean vector the same length as the This allows you to select rows where one or more columns have values you want: . Whereas, when we extracted portions of a pandas dataframe like we did earlier, we got a two-dimensional DataFrame type of object. Just something to keep in mind for later. So, the formula to extract a column is still the same, but this time we didn’t pass any index name before and after the first colon. Not passing anything tells Python to

pandas.DataFrame.fillna — pandas 1.1.0 documentation, Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a� While working with data in Pandas, we perform a vast array of operations on the data to get the data in the desired form. One of these operations could be that we want to create new columns in the DataFrame based on the result of some operations on the existing columns in the DataFrame.

pandas.DataFrame.last — pandas 1.1.0 documentation, For instance, '3D' will display all the rows having their index within the last 3 days. Returns Select values between particular times of the day. Examples. Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row). Parameters periods int, default 1. Periods to shift for calculating difference, accepts negative values. axis {0 or ‘index’, 1 or ‘columns’}, default 0. Take difference over rows (0) or columns (1

Comments
  • Thanks for sharing this solution. but I have many columns and I wanted to apply this to only null values.