You can read the previous part of the article here .
11.Delete column
If you want to drop one or more columns from the DataFrame, use the drop() method as shown below:
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import pandas as pd df = pd.DataFrame([[1,"A"], [2,"B"]], columns = [“coli", "“col2"]) df.drop(columns = ["col2"]) """ col1 0 1 l 2 """ |
Read more here .
12. GroupBy:
If you want to perform an aggregate operation after grouping, use the groupby() method as shown below:
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import pandas as pd df = pd.DataFrame([[1,"A"], [2,"B"], [3,"A"], [4,"C"]], columns = ["col1", "col2"]) df.groupby("col2").col1.sum() """ Col2 A 4 B 2 C 4 """ |
Read more here .
13. Unique value in column:
If you want to count or print unique values in a column of DataFrame, use unique() or nunique() method as shown below:
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import pandas as pd df = pd.DataFrame([[1,"A"], [2,"B"], [3,"A"], [4,"C"]], columns = ["col1", "col2"]) # Print Unique values df.col2.unique() """ ['A','B','C'] """ # Number of unique values df.col2.nunique() """ 3 """ |
Read more here .
14. Fill in NaN (empty) values
If you want to replace the NaN values in a column with some other value, use the fillna() method as shown below:
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import pandas as pd import numpy as np df = pd.DataFrame([[1, "A"], [2, np.nan], [3, np.nan], columns = ["col1", "col2"]) df.col2.fillna("B", inplace = True) """ col1 col2 0 1 A 1 2 B 2 3 B """ |
Read more here .
15. Apply function on 1 column:
If you want to apply a function to a column, use the apply() method as shown below:
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import pandas as pd def f(number): return number + 2 df = pd.DataFrame([[1, "A"], [2, "B"], columns = ["col1", "col2"]) df["col3"] = df.col1.apply(f) """ col1 col2 col3 0 1 A 3 1 2 B 4 """ |
Read more here .
16. Remove duplicates:
If you want to remove duplicate values, use the dropduplicates() method as shown below:
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import pandas as pd df = pd.DataFrame([[1,"A"], [2,"B"], [1,"A"], columns = ["col1", "col2"]) df.drop_duplicates() """ col1 col2 0 1 A 1 2 B """ |
Read more here .
17. Counting values:
If you want to find the frequency of each value in a column, use the value_counts() method as shown below:
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import pandas as pd df = pd.DataFrame([[1,"A"], [2,"B"], [2,"A"], [3,"C"]], columns = ["col1", "col2"]) df.col2.value_counts() """ A 2 B 1 C 1 """ |
18. Size of DataFrame:
If you want to find the size of the DataFrame, use the .shape property as shown below:
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import pandas as pd df = pd.DataFrame([[1,"A"], [2,"B"], [2,"A"], [3,"C"]], columns = ["col1", "col2"]) df.shape """ (4,2) """ |
To wrap up, in this post, I’ve covered some of the most commonly used functions/methods in Pandas to get you started with this library.
Furthermore, there is no better place than to consult the official Pandas documentation available here to get a basic and practical knowledge of the different methods in Pandas. The official Pandas documentation provides a detailed explanation of each argument accepted by a function along with a practical example which is, in my opinion, a great way to gain Pandas expertise.
Thanks for reading. I hope this article was helpful.