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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

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    do you get warning when you don't use .copy()? If not then you don't need copy. And if you get warning then use copy. So maybe let pandas let you know when you need copy. Commented Sep 28 at 15:34
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    it seems you ask 3 different questions. .copy(), dtype and .str. You should ask only one question on one page. Commented Sep 28 at 15:38
  • yeah sorry for that !! but the main doubt i have is in .copy() although i dont get any error but like i was just having a gap in my understanding that if i am doing df=df[mask].copy() whats the benefit anyway i am like modifying / overwriting the df itself so was confused whether it should be used and when to use it when to not and also i think in terms of even if i use a copy will there be any overhead in any sense in that case ? Commented Sep 28 at 16:08
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    It's impossible to say whether copy is needed unless you show the (realistic) code that occurs after the filter. Commented Sep 28 at 16:13
  • ummm actually maybe that wont help much as it is not in jupyter ...but if u say i ll provide..Also by interpreting your response i can think of it as it depends on our query if we got an error or not ? Commented Sep 28 at 16:16

1 Answer 1

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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.

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4 Comments

Thats nice but as i am very new to all this ...i didnt get much of your response and by how much i got is u meant to say by is_view we can confirm is it from original df or not and perhaps use copy or not as everytime we cant make a copy too ?? am i right ?
like basically not getting when to use copy and when to not
Updated, the answer. Hope that helps.
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: pandas.pydata.org/pandas-docs/stable/user_guide/… and like u said here is_view was f

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