Replace values from second row onwards in a pandas pipe method
Question:
I am wondering how to replace values from second row onwards in a pipe method (connecting to the rest of steps).
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Date": ["2020-01-01", "2021-01-01", "2022-01-01"],
"Pop": [90, 70, 60],
}
)
Date Pop
0 2020-01-01 90
1 2021-01-01 70
2 2022-01-01 60
Current solution
df.iloc[1:] = np.nan
Expected output
Date Pop
0 2020-01-01 90
1 2021-01-01 NaN
2 2022-01-01 NaN
Answers:
To replace the values from the second row onwards in your DataFrame using the pandas method pipe, you can do the following:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Date": ["2020-01-01", "2021-01-01", "2022-01-01"],
"Pop": [90, 70, 60],
}
)
# Use the `pipe` method to apply a function to your DataFrame
df = df.pipe(lambda x: x.iloc[1:]).replace(np.nan)
print(df)
This will replace the values from the second row onwards in your DataFrame with NaN. The resulting DataFrame will look like this:
Date Pop
0 2021-01-01 NaN
1 2022-01-01 NaN
Note that this will modify your original DataFrame in place, so if you want to keep the original DataFrame, you should make a copy of it before applying the pipe method. You can do this by using the copy method, like this:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Date": ["2020-01-01", "2021-01-01", "2022-01-01"],
"Pop": [90, 70, 60],
}
)
# Make a copy of the original DataFrame
df_copy = df.copy()
# Use the `pipe` method to apply a function to your DataFrame
df_copy = df_copy.pipe(lambda x: x.iloc[1:]).replace(np.nan)
print(df_copy)
This will create a copy of your DataFrame and then modify the copy, so the original DataFrame will remain unchanged. The resulting DataFrame will look the same as in the previous example
You can also use assign
like this:
df.assign(Pop=df.loc[[0], 'Pop'])
Output:
Date Pop
0 2020-01-01 90.0
1 2021-01-01 NaN
2 2022-01-01 NaN
Note: assign
works with nice column headers, if your headers have spaces or special characters you will need to use a different method.
I am wondering how to replace values from second row onwards in a pipe method (connecting to the rest of steps).
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Date": ["2020-01-01", "2021-01-01", "2022-01-01"],
"Pop": [90, 70, 60],
}
)
Date Pop
0 2020-01-01 90
1 2021-01-01 70
2 2022-01-01 60
Current solution
df.iloc[1:] = np.nan
Expected output
Date Pop
0 2020-01-01 90
1 2021-01-01 NaN
2 2022-01-01 NaN
To replace the values from the second row onwards in your DataFrame using the pandas method pipe, you can do the following:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Date": ["2020-01-01", "2021-01-01", "2022-01-01"],
"Pop": [90, 70, 60],
}
)
# Use the `pipe` method to apply a function to your DataFrame
df = df.pipe(lambda x: x.iloc[1:]).replace(np.nan)
print(df)
This will replace the values from the second row onwards in your DataFrame with NaN. The resulting DataFrame will look like this:
Date Pop
0 2021-01-01 NaN
1 2022-01-01 NaN
Note that this will modify your original DataFrame in place, so if you want to keep the original DataFrame, you should make a copy of it before applying the pipe method. You can do this by using the copy method, like this:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Date": ["2020-01-01", "2021-01-01", "2022-01-01"],
"Pop": [90, 70, 60],
}
)
# Make a copy of the original DataFrame
df_copy = df.copy()
# Use the `pipe` method to apply a function to your DataFrame
df_copy = df_copy.pipe(lambda x: x.iloc[1:]).replace(np.nan)
print(df_copy)
This will create a copy of your DataFrame and then modify the copy, so the original DataFrame will remain unchanged. The resulting DataFrame will look the same as in the previous example
You can also use assign
like this:
df.assign(Pop=df.loc[[0], 'Pop'])
Output:
Date Pop
0 2020-01-01 90.0
1 2021-01-01 NaN
2 2022-01-01 NaN
Note: assign
works with nice column headers, if your headers have spaces or special characters you will need to use a different method.