There are several ways to implement it! Here is a sample dataset:
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({
'A': [1, 2, 3, 4],
'B': [None, 5, None, 7]
})
1. pd.Series()
# Convert the index to a Series like a column of the DataFrame
df["UID"] = pd.Series(df.index).apply(lambda x: "UID_" + str(x).zfill(6))
print(df)
output:
UID A B
0 UID_000000 1 NaN
1 UID_000001 2 5.0
2 UID_000002 3 NaN
3 UID_000003 4 7.0
2. list
# Do the operation in the list
df["UID"] = ["UID_" + str(ind).zfill(6) for ind in list(df.index)]
print(df)
3. df.reset_index()
# Reset the index and create a new column 'ID' from the index
df = df.reset_index().rename(columns={'index': 'UID'})
# Add the prefix 'UID_' to the ID values
df['UID'] = 'UID_' + df['UID'].astype(str).apply(lambda x: x.zfill(6))
print(df)
The reset_index()
function in pandas is used to reset the index of a DataFrame. By default, it resets the index to the default integer index and converts the old index into a column.
标签:index,UID,df,1060,Create,DataFrame,pd,ID From: https://www.cnblogs.com/alex-bn-lee/p/18408054