pythonpandascopystartswith

When should I use .copy() after filtering a DataFrame in pandas?

I am working on a project where I frequently filter my DataFrame and then return it for further processing. For example, in one file I have this code:

df = df[df['Ticker'].str.startswith("NATURALGAS", na=False)].copy()

I am a bit confused about the following points:
Why is .copy() needed here?
I read that without .copy(), pandas creates a “view” instead of a full DataFrame, which may cause SettingWithCopyWarning. But I don’t clearly understand what this “view” means and how it can cause problems in real usage and then pass df2 into another function where I modify it. Should I always add .copy() in such cases?

What if I only filter and immediately return the result, without modifying it — do I still need .copy()? Moreover, when I read my CSV, the Ticker column is loaded as object dtype by default.

Should I explicitly convert it to string dtype? Does this make any difference in handling NaN values with .str.contains() or .str.startswith()?

import pandas as pd

s = pd.Series(["NATURALGAS24JANFUT", None, "CRUDEOIL24JANFUT"])

# Filtering
mask = s.str.startswith("NATURALGAS", na=False)
filtered = s[mask]
print(filtered)

As I tried it with _is_view also but still getting an error df1=pd.read_csv('2025/JAN_25/01012025/01.csv',usecols=['Ticker','Date','Time','Open','High','Low','Close']) df1= df1[df1['Ticker'].str.startswith("NATURALGAS")] df1._is_view
#false df1["Name"]="John" C:\Users\Asus\AppData\Local\Temp\ipykernel_17352\1487562051.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy and like u said here is_view was false

Solution

  • df._is_view will show if a df is a view or not. Also works for series s._is_view.

    For most general cases, copies work fine. Views are most helpful for optimization and workflows with large datasets where multiple copies of the data is impractical. SettingWithCopyWarning arises so developers are aware that changes are being applied to a copy, not the original data. These types of warnings help ensure changes/filtering/modifications are being set on the correct dataframe.

    In general, .copy() will not be needed.

    Extended Code Example:

    import pandas as pd

data = {
    "Name": ["Alice", None, "Charlie", "Alex", None],
    "Age": [25, 30, 35, 40, 45],
    "Score": [88.5, 92.0, 79.5, 85.0, 91.5]
}

# Create df
df = pd.DataFrame(data)
df._is_view
# False

df2 = df
df2._is_view
# False

# Here a view is created representing a subset of df
df3 = df['Name']
df3._is_view
# True

# Adding copy
df4 = df['Name'].copy()
df4._is_view
# False

    Explanation

    In the above, df, df2, df3, and df4 are all pandas dataframe. If you add ._is_view to any and run that code, it will return True if it is a view and False if is not. Note: in the code section, lines starting with # show the output of that line of code.

    Adding .copy() is not necessary in many cases, because the default behavior of pandas already returns a copy. It will not hurt, but it is not necessary.

    In the above, df3 = df['Name'] creates a view. In this case, adding .copy() makes a difference. df4 = df['Name'].copy(). This is shown by df3._is_view returning True (confirming it is a view), while df4._is_view returns False.