pandas pass two columns to function
#Method 1:
df["Delivery Charges"] = df[["Weight", "Package Size", "Delivery Mode"]].apply(
lambda x : calculate_rate(*x), axis=1)
#Method 2:
df["Delivery Charges"] = df.apply(
lambda x : calculate_rate(x["Weight"],
x["Package Size"], x["Delivery Mode"]), axis=1)python add multiple columns to pandas dataframe
# Basic syntax:
df[['new_column_1_name', 'new_column_2_name']] = pd.DataFrame([[np.nan, 'word']], index=df.index)
# Where the columns you're adding have to be pandas dataframes
# Example usage:
# Define example dataframe:
import pandas as pd
import numpy as np
df = pd.DataFrame({
'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]
})
print(df)
col_1 col_2
0 0 4
1 1 5
2 2 6
3 3 7
# Add several columns simultaneously:
df[['new_col_1', 'new_col_2', 'new_col_3']] = pd.DataFrame([[np.nan, 42, 'wow']], index=df.index)
print(df)
col_1 col_2 new_col_1 new_col_2 new_col_3
0 0 4 NaN 42 wow
1 1 5 NaN 42 wow
2 2 6 NaN 42 wow
3 3 7 NaN 42 wow
# Note, this isn't much more efficient than simply doing three
# separate assignments, e.g.:
df['new_col_1'] = np.nan
df['new_col_2'] = 42
df['new_col_3'] = 'wow'assign multiple columns pandas
import pandas as pd
df = {'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]}
df = pd.DataFrame(df)
df[[ 'column_new_1', 'column_new_2','column_new_3']] = [np.nan, 'dogs',3] #thought this woapply a function to multiple columns in pandas
In [49]: df
Out[49]:
0 1
0 1.000000 0.000000
1 -0.494375 0.570994
2 1.000000 0.000000
3 1.876360 -0.229738
4 1.000000 0.000000
In [50]: def f(x):
....: return x[0] + x[1]
....:
In [51]: df.apply(f, axis=1) #passes a Series object, row-wise
Out[51]:
0 1.000000
1 0.076619
2 1.000000
3 1.646622
4 1.000000Copyright © 2021 Codeinu
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