面试题与答案

A DataFrame is a 2-dimensional labeled data structure with columns that can be of different types. It is similar to a spreadsheet or SQL table.

loc is label-based indexing, and iloc is integer-based indexing.

groupby is used to split the data into groups based on some criteria and then apply a function to each group independently.

Example:

df.groupby('column1').mean()

df.dropna() or df.fillna(value)

merge is used to combine two DataFrames based on a common column or index.

Example:

pd.merge(df1, df2, on='common_column')

melt is used to transform wide-format data to long-format data.

Example:

pd.melt(df, id_vars=['id_column'], value_vars=['value_column'])

Broadcasting is the ability of NumPy and Pandas to perform operations on arrays or DataFrames of different shapes.

concat is used to concatenate DataFrames along a particular axis.

Example:

pd.concat([df1, df2], axis=1)

nunique returns the number of unique elements in a Series or DataFrame.

Example:

df['column'].nunique()

cut is used to segment and sort data values into bins.

Example:

pd.cut(df['column'], bins=[0, 25, 50, 75, 100])

Method chaining is a way of applying multiple operations on a DataFrame in a single line of code.

Example:

df.dropna().mean()

iterrows() is used to iterate over DataFrame rows as (index, Series) pairs.

Example:

for index, row in df.iterrows():
    print(index, row['column'])

get_dummies() is used to convert categorical variable(s) into dummy/indicator variables.

Example:

pd.get_dummies(df['column'])

Series.value_counts() returns the counts of unique values in a Series, while DataFrame['column'].value_counts() returns counts for a specific column.

pd.to_numeric() is used to convert argument to a numeric type.

Example:

df['column'] = pd.to_numeric(df['column'], errors='coerce')

pd.cut() is used to segment and sort data values into bins. The `bins` parameter defines the bin edges.

Example:

pd.cut(df['column'], bins=[0, 25, 50, 75, 100])

Use the on parameter with a list of column names. pd.merge(df1, df2, on=['column1', 'column2'])

Use the pivot() function to reshape the DataFrame based on column values.

Example:

df.pivot(index='index_column', columns='column_to_pivot', values='value_column')

crosstab() computes a simple cross-tabulation of two (or more) factors.

Example:

pd.crosstab(df['factor1'], df['factor2'])

Use the applymap() function. df.applymap(my_function)

Method chaining is a way of applying multiple operations on a DataFrame in a single line of code.

Example:

df.dropna().mean()