python if pandas dataframe is empty
Jul 13, 2025 am 01:48 AMThe most common method to determine whether a Pandas DataFrame is empty is to use the df.empty property. 1. df.empty is the preferred method to determine whether DataFrame is empty. It returns a Boolean value, and if there is no data, it is True; 2. Common misjudgments include the result of filtering and the empty check should also be used at this time; 3. Other methods such as len(df) == 0 or df.shape[0] == 0 Although feasible, the readability and stability are slightly poor, it is recommended to use df.empty first.
The most common method to determine whether a Pandas DataFrame is empty is to use the empty
attribute. If you want to know how to quickly confirm whether a DataFrame has data, using this property directly is the easiest and most effective way.

How to determine whether DataFrame is empty
Pandas provides a special property: df.empty
, which returns a boolean value. If there is no data in the DataFrame (that is, the number of rows is 0), True
will be returned; otherwise, False
will be returned.
import pandas as pd df = pd.DataFrame() if df.empty: print("DataFrame is empty") else: print("DataFrame has data")
This method is concise and clear, and is suitable for use when making conditional judgments in programs, such as checking whether it is loaded successfully after reading data.

Common misjudgment situations and precautions
Sometimes you think the DataFrame is empty, but in fact, the result is filtered out because of some operations. For example, in the following situation:
df = pd.DataFrame({'A': [1, 2, 3]}) filtered_df = df[df['A'] > 10] # This filter will not match any rows
At this time filtered_df
is an empty DataFrame. If you don't pay attention to this, you may make an error in subsequent processing.

Therefore, when writing code, it is recommended to add a judgment after the key steps:
- Check if it is empty after reading the CSV or database
- After filtering, grouping and other operations, you should also pay attention to whether there is data in the results.
This can avoid errors during runtime and improve the robustness of the program.
Other methods can be judged, but not recommended
Although there are other ways to determine whether the DataFrame is empty, for example:
-
len(df) == 0
-
df.shape[0] == 0
-
not df.index.tolist()
These methods can also achieve their goals, but in comparison:
- Not as intuitive as
df.empty
- Slightly poor readability
- It may not be stable enough under certain boundary situations
So if you don’t have special needs, it is recommended to use df.empty
first.
Basically that's it. It is not complicated to judge whether the DataFrame is empty, but it is easy to cause errors due to negligence in actual projects. Adding a judgment in advance can save a lot of trouble.
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