[Python] Pandas Library | Pandas 라이브러리

Pandas Library

 

Pandas 라이브러리

 

 

 

- Python Data Analysis의 약어이다.

- 관계형 또는 레이블된 데이터를 직관적으로 조작할 수 있도록 하는 데이터 구조를 제공하는 라이브러리이다.

 

- Pandas는 아래와 같은 종류의 데이터들에 대한 분석에 적합하다:

• SQL Table 또는 Excel Spreadsheet
• 정렬되었거나 정렬되지 않은 시계열 데이터
• 다른 형태의 관찰 / 통계 데이터 시트

 

- Pandas에서는 용이한 데이터 분석을 위해 아래와 같은 데이터 구조를 제공하고 있다:

• Series (시리즈)
  - 1-Dimensional Array
  - Index를 적용할 수 있다.
   
• DataFrame (데이터프레임)
  - 2-Dimensional Array (Row X Column)
  - 각 Column은 서로 다른 데이터 타입을 가질 수 있다.

 

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Installation (설치)

 

Installation Command (설치 명령어)

# Installing from PyPI
pip install pandas

# Installing from Miniconda
conda install pandas

# Installing using Linux Distribution's Package Manager
sudo apt-get install python3-pandas # Ubuntu, Debian
yum install python3-pandas          # CentOS

 

 

 

Import (모듈 임포트)

# Recommended import style
import pandas as pd

 

 

 

Version Check (버전 체크)

import pandas
pandas.__version__

 

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Input/Output Methods

 

- Pandas에서는 데이터 입출력에 아래와 같은 함수들을 제공하고 있다.

 

Category	Methods	Description
Pickling	read_pickle(filepath_or_buffer[, ...])	- 파일로부터 pickled pandas 객체를 읽어온다.
	DataFrame.to_pickle(path[, compression, ...])	- Pickle (serialize) 객체를 파일에 쓴다.
Flat file	read_table(filepath_or_buffer[, sep, ...])	- general delimited file을 읽어   DataFrame으로 반환한다.
	read_csv(filepath_or_buffer[, sep, ...])	- comma-separated values (csv) file을 읽어   DataFrame으로 반환한다.
	DataFrame.to_csv([path_or_buf, sep, na_rep, ...])	- comma-separated values를 csv file로 쓴다.
	read_fwf(filepath_or_buffer[, colspecs, ...])	- fixed-width formatted lines으로 구성된   table을 읽어 DataFrame로 반환한다.
Clipboard	read_clipboard([sep])	- clipboard로부터 텍스트를 읽어   read_csv() 함수에 넘긴다.
	DataFrame.to_clipboard([excel, sep])	- 객체를 복사하여 system clipboard에 저장한다.
Excel	read_excel(io[, sheet_name, header, names, ...])	- Excel file을 읽어   pandas DataFrame으로 반환한다.
	DataFrame.to_excel(excel_writer[, ...])	- 객체를 Excel sheet에 쓴다.
	ExcelFile.parse([sheet_name, header, names, ...])	- 특정 Excel sheet(s)를 Parse하여   DataFrame로 반환한다.
	Styler.to_excel(excel_writer[, sheet_name, ...])	- Styler를 Excel sheet에 쓴다.
	ExcelWriter(path[, engine, date_format, ...])	- Excel sheet에 DataFrame을 쓰기 위한   Class이다.
JSON	read_json([path_or_buf, orient, typ, dtype, ...])	- JSON string을 pandas 객체로 반환한다.
	json_normalize(data[, record_path, meta, ...])	- Normalize semi-structured JSON data를   flat table에 저장한다.
	DataFrame.to_json([path_or_buf, orient, ...])	- 객체를 JSON string으로 반환한다.
	build_table_schema(data[, index, ...])	- 데이터로부터   Table schema를 Build하여 반환한다.
HTML	read_html(io[, match, flavor, header, ...])	- HTML tables을 읽어   DataFrame 객체로 구성된 List로 반환한다.
	DataFrame.to_html([buf, columns, col_space, ...])	- DataFrame을 HTML table로 변환한다.
	Styler.to_html([buf, table_uuid, ...])	- buffer or string in HTML-CSS format의   buffer 혹은 string으로 구성된   Styler를 파일에 쓴다.
XML	read_xml(path_or_buffer[, xpath, ...])	- XML을 DataFrame로 반환한다.
	DataFrame.to_xml([path_or_buffer, index, ...])	- DataFrame을 XML로 반환한다.
SQL	read_sql_table(table_name, con[, schema, ...])	- SQL database table을 DataFrame로 반환한다.
	read_sql_query(sql, con[, index_col, ...])	- SQL query를 DataFrame로 반환한다.
	read_sql(sql, con[, index_col, ...])	- SQL query 또는 database table을   DataFrame로 반환한다.
	DataFrame.to_sql(name, con[, schema, ...])	- DataFrame에 저장된 records를   SQL database에 쓴다.

 

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General Functions

 

Category	Function	Description
Data Manipulations	melt(frame[, id_vars, value_vars, var_name, ...])	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
	pivot(data[, index, columns, values])	- 지정한 index와 column값에 맞추어   reshape된 DataFrame을 반환한다.
	pivot_table(data[, values, index, columns, ...])	- 데이터를 spreadsheet-style pivot table의   DataFrame으로 반환한다.
	crosstab(index, columns[, values, rownames, ...])	Compute a simple cross tabulation of two (or more) factors.
	cut(x, bins[, right, labels, retbins, ...])	Bin values into discrete intervals.
	qcut(x, q[, labels, retbins, precision, ...])	Quantile-based discretization function.
	merge(left, right[, how, on, left_on, ...])	- DataFrame 또는 named Series를   Database에서의 Join과 같이 병합한다.
	merge_ordered(left, right[, on, left_on, ...])	Perform a merge for ordered data with optional filling/interpolation.
	merge_asof(left, right[, on, left_on, ...])	Perform a merge by key distance.
	concat(objs[, axis, join, ignore_index, ...])	Concatenate pandas objects along a particular axis with optional set logic along the other axes.
	get_dummies(data[, prefix, prefix_sep, ...])	Convert categorical variable into dummy/indicator variables.
	factorize(values[, sort, na_sentinel, size_hint])	Encode the object as an enumerated type or categorical variable.
	unique(values)	Return unique values based on a hash table.
	wide_to_long(df, stubnames, i, j[, sep, suffix])	Unpivot a DataFrame from wide to long format.
Top-Level Missing Data	isna(obj)	Detect missing values for an array-like object.
	isnull(obj)	Detect missing values for an array-like object.
	notna(obj)	Detect non-missing values for an array-like object.
	notnull(obj)	Detect non-missing values for an array-like object.
Top-Level dealing with Numeric Data	to_numeric(arg[, errors, downcast])	Convert argument to a numeric type.
Top-Level dealing with Datetimelike Data	to_datetime(arg[, errors, dayfirst, ...])	Convert argument to datetime.
	to_timedelta(arg[, unit, errors])	Convert argument to timedelta.
	date_range([start, end, periods, freq, tz, ...])	Return a fixed frequency DatetimeIndex.
	bdate_range([start, end, periods, freq, tz, ...])	Return a fixed frequency DatetimeIndex, with business day as the default frequency.
	period_range([start, end, periods, freq, name])	Return a fixed frequency PeriodIndex.
	timedelta_range([start, end, periods, freq, ...])	Return a fixed frequency TimedeltaIndex, with day as the default frequency.
	infer_freq(index[, warn])	Infer the most likely frequency given the input index.
Top-Level dealing with Interval Data	interval_range([start, end, periods, freq, ...])	Return a fixed frequency IntervalIndex.
Top-Level Evaluation	eval(expr[, parser, engine, truediv, ...])	Evaluate a Python expression as a string using various backends.
Hashing	util.hash_array(vals[, encoding, hash_key, ...])	Given a 1d array, return an array of deterministic integers.
	util.hash_pandas_object(obj[, index, ...])	Return a data hash of the Index/Series/DataFrame.
Testing	test([extra_args])	Run the pandas test suite using pytest.

 

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Series (시리즈)

 

- Pandas에서 제공하는 1차원 배열 데이터 구조이다.

- Indexing 및 Slicing이 가능하며, 문자 Index를 부여 및 접근할 수 있다.

- 데이터 타입(dtype)이 존재한다.

 

Series Attributes

Category	Attribute	Description
Axes (Axis)	Series.index	- 해당 Series의 Index를 반환한다. - Series의 Index를 지정할 수 있다.
	Series.array	The ExtensionArray of the data backing this Series or Index.
	Series.values	Return Series as ndarray or ndarray-like depending on the dtype.
	Series.dtype	Return the dtype object of the underlying data.
	Series.shape	Return a tuple of the shape of the underlying data.
	Series.nbytes	Return the number of bytes in the underlying data.
	Series.ndim	Number of dimensions of the underlying data, by definition 1.
	Series.size	Return the number of elements in the underlying data.
	Series.T	Return the transpose, which is by definition self.
	Series.hasnans	Return True if there are any NaNs.
	Series.empty	Indicator whether Series/DataFrame is empty.
	Series.dtypes	Return the dtype object of the underlying data.
	Series.name	Return the name of the Series.
	Series.flags	Get the properties associated with this pandas object.
Indexting & Iteration	Series.at	Access a single value for a row/column label pair.
	Series.iat	Access a single value for a row/column pair by integer position.
	Series.loc	Access a group of rows and columns by label(s) or a boolean array.
	Series.iloc	Purely integer-location based indexing for selection by position.

 

 

Series Methods

Category	Method	Description
Constructor	Series([data, index, dtype, name, copy, ...])	- axis label이 부여된 1차원 ndarray 객체를   생성한다. - index에 내가 원하는 Index 이름을   지정할 수 있다.
Axes (Axis)	Series.memory_usage([index, deep])	- Series의 메모리 사용량을 반환한다.
	Series.set_flags(*[, copy, ...])	Return a new object with updated flags.
Conversion	Series.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
	Series.convert_dtypes([infer_objects, ...])	Convert columns to best possible dtypes using dtypes supporting pd.NA.
	Series.infer_objects()	Attempt to infer better dtypes for object columns.
	Series.copy([deep])	Make a copy of this object's indices and data.
	Series.bool()	Return the bool of a single element Series or DataFrame.
	Series.to_numpy([dtype, copy, na_value])	A NumPy ndarray representing the values in this Series or Index.
	Series.to_period([freq, copy])	Convert Series from DatetimeIndex to PeriodIndex.
	Series.to_timestamp([freq, how, copy])	Cast to DatetimeIndex of Timestamps, at beginning of period.
	Series.to_list()	Return a list of the values.
	Series.__array__([dtype])	Return the values as a NumPy array.
Indexing and Iteration	Series.get(key[, default])	Get item from object for given key (ex: DataFrame column).
	Series.__iter__()	Return an iterator of the values.
	Series.items()	Lazily iterate over (index, value) tuples.
	Series.iteritems()	Lazily iterate over (index, value) tuples.
	Series.keys()	Return alias for index.
	Series.pop(item)	Return item and drops from series.
	Series.item()	Return the first element of the underlying data as a Python scalar.
	Series.xs(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.

 

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DataFrame (데이터프레임)

 

DataFrame Attributes

Category	Attribute	Description
Constructor	DataFrame([data, index, columns, dtype, copy])	- Two-dimensional, size-mutable, potentially heterogeneous tabular data.
Axes (Axis)	DataFrame.index	- DataFrame의 row labels(index)을 반환한다.
	DataFrame.columns	- DataFrame의 column labels을 반환한다.
	DataFrame.dtypes	Return the dtypes in the DataFrame.
	DataFrame.values	Return a Numpy representation of the DataFrame.
	DataFrame.axes	Return a list representing the axes of the DataFrame.
	DataFrame.ndim	Return an int representing the number of axes / array dimensions.
	DataFrame.size	Return an int representing the number of elements in this object.
	DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
	DataFrame.empty	Indicator whether Series/DataFrame is empty.
Indexing & Iteration	DataFrame.at	Access a single value for a row/column label pair.
	DataFrame.iat	Access a single value for a row/column pair by integer position.
	DataFrame.loc	Access a group of rows and columns by label(s) or a boolean array.
	DataFrame.iloc	Purely integer-location based indexing for selection by position.
Reshaping & Sorting & Transposing	DataFrame.T	- 전치된 행렬(Transposed Matrix)을 반환한다.
Metadata	DataFrame.attrs	A dictionary for storing global metadata for this DataFrame.

 

 

DataFrame Methods

Category	Method	Description
Axes (Axis)	DataFrame.info([verbose, buf, max_cols, ...])	Print a concise summary of a DataFrame.
	DataFrame.select_dtypes([include, exclude])	Return a subset of the DataFrame's columns based on the column dtypes.
	DataFrame.memory_usage([index, deep])	Return the memory usage of each column in bytes.
	DataFrame.set_flags(*[, copy, ...])	Return a new object with updated flags.
Conversion	DataFrame.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
	DataFrame.convert_dtypes([infer_objects, ...])	Convert columns to best possible dtypes using dtypes supporting pd.NA.
	DataFrame.infer_objects()	Attempt to infer better dtypes for object columns.
	DataFrame.copy([deep])	Make a copy of this object's indices and data.
	DataFrame.bool()	Return the bool of a single element Series or DataFrame.
Indexing & Iteration	DataFrame.head([n])	Return the first n rows.
	DataFrame.insert(loc, column, value[, ...])	Insert column into DataFrame at specified location.
	DataFrame.__iter__()	Iterate over info axis.
	DataFrame.items()	Iterate over (column name, Series) pairs.
	DataFrame.iteritems()	Iterate over (column name, Series) pairs.
	DataFrame.keys()	Get the 'info axis' (see Indexing for more).
	DataFrame.iterrows()	Iterate over DataFrame rows as (index, Series) pairs.
	DataFrame.itertuples([index, name])	Iterate over DataFrame rows as namedtuples.
	DataFrame.pop(item)	Return item and drop from frame.
	DataFrame.tail([n])	Return the last n rows.
	DataFrame.xs(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.
	DataFrame.get(key[, default])	Get item from object for given key (ex: DataFrame column).
	DataFrame.isin(values)	Whether each element in the DataFrame is contained in values.
	DataFrame.where(cond[, other, inplace, ...])	Replace values where the condition is False.
	DataFrame.mask(cond[, other, inplace, axis, ...])	Replace values where the condition is True.
	DataFrame.query(expr[, inplace])	Query the columns of a DataFrame with a boolean expression.
Binary Operator Functions	DataFrame.add(other[, axis, level, fill_value])	Get Addition of dataframe and other, element-wise (binary operator add).
	DataFrame.sub(other[, axis, level, fill_value])	Get Subtraction of dataframe and other, element-wise (binary operator sub).
	DataFrame.mul(other[, axis, level, fill_value])	Get Multiplication of dataframe and other, element-wise (binary operator mul).
	DataFrame.div(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
	DataFrame.truediv(other[, axis, level, ...])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
	DataFrame.floordiv(other[, axis, level, ...])	Get Integer division of dataframe and other, element-wise (binary operator floordiv).
	DataFrame.mod(other[, axis, level, fill_value])	Get Modulo of dataframe and other, element-wise (binary operator mod).
	DataFrame.pow(other[, axis, level, fill_value])	Get Exponential power of dataframe and other, element-wise (binary operator pow).
	DataFrame.dot(other)	Compute the matrix multiplication between the DataFrame and other.
	DataFrame.radd(other[, axis, level, fill_value])	Get Addition of dataframe and other, element-wise (binary operator radd).
	DataFrame.rsub(other[, axis, level, fill_value])	Get Subtraction of dataframe and other, element-wise (binary operator rsub).
	DataFrame.rmul(other[, axis, level, fill_value])	Get Multiplication of dataframe and other, element-wise (binary operator rmul).
	DataFrame.rdiv(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
	DataFrame.rtruediv(other[, axis, level, ...])	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
	DataFrame.rfloordiv(other[, axis, level, ...])	Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).
	DataFrame.rmod(other[, axis, level, fill_value])	Get Modulo of dataframe and other, element-wise (binary operator rmod).
	DataFrame.rpow(other[, axis, level, fill_value])	Get Exponential power of dataframe and other, element-wise (binary operator rpow).
	DataFrame.lt(other[, axis, level])	Get Less than of dataframe and other, element-wise (binary operator lt).
	DataFrame.gt(other[, axis, level])	Get Greater than of dataframe and other, element-wise (binary operator gt).
	DataFrame.le(other[, axis, level])	Get Less than or equal to of dataframe and other, element-wise (binary operator le).
	DataFrame.ge(other[, axis, level])	Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
	DataFrame.ne(other[, axis, level])	Get Not equal to of dataframe and other, element-wise (binary operator ne).
	DataFrame.eq(other[, axis, level])	Get Equal to of dataframe and other, element-wise (binary operator eq).
	DataFrame.combine(other, func[, fill_value, ...])	Perform column-wise combine with another DataFrame.
	DataFrame.combine_first(other)	Update null elements with value in the same location in other.
Function Application & GroupBy & window	DataFrame.apply(func[, axis, raw, ...])	Apply a function along an axis of the DataFrame.
	DataFrame.applymap(func[, na_action])	Apply a function to a Dataframe elementwise.
	DataFrame.pipe(func, *args, **kwargs)	Apply chainable functions that expect Series or DataFrames.
	DataFrame.agg([func, axis])	Aggregate using one or more operations over the specified axis.
	DataFrame.aggregate([func, axis])	Aggregate using one or more operations over the specified axis.
	DataFrame.transform(func[, axis])	Call func on self producing a DataFrame with the same axis shape as self.
	DataFrame.groupby([by, axis, level, ...])	Group DataFrame using a mapper or by a Series of columns.
	DataFrame.rolling(window[, min_periods, ...])	Provide rolling window calculations.
	DataFrame.expanding([min_periods, center, ...])	Provide expanding window calculations.
	DataFrame.ewm([com, span, halflife, alpha, ...])	Provide exponentially weighted (EW) calculations.
Computations & Descriptive Stats	DataFrame.abs()	Return a Series/DataFrame with absolute numeric value of each element.
	DataFrame.all([axis, bool_only, skipna, level])	Return whether all elements are True, potentially over an axis.
	DataFrame.any([axis, bool_only, skipna, level])	Return whether any element is True, potentially over an axis.
	DataFrame.clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
	DataFrame.corr([method, min_periods])	Compute pairwise correlation of columns, excluding NA/null values.
	DataFrame.corrwith(other[, axis, drop, method])	Compute pairwise correlation.
	DataFrame.count([axis, level, numeric_only])	Count non-NA cells for each column or row.
	DataFrame.cov([min_periods, ddof])	Compute pairwise covariance of columns, excluding NA/null values.
	DataFrame.cummax([axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
	DataFrame.cummin([axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
	DataFrame.cumprod([axis, skipna])	Return cumulative product over a DataFrame or Series axis.
	DataFrame.cumsum([axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
	DataFrame.describe([percentiles, include, ...])	Generate descriptive statistics.
	DataFrame.diff([periods, axis])	First discrete difference of element.
	DataFrame.eval(expr[, inplace])	Evaluate a string describing operations on DataFrame columns.
	DataFrame.kurt([axis, skipna, level, ...])	Return unbiased kurtosis over requested axis.
	DataFrame.kurtosis([axis, skipna, level, ...])	Return unbiased kurtosis over requested axis.
	DataFrame.mad([axis, skipna, level])	Return the mean absolute deviation of the values over the requested axis.
	DataFrame.max([axis, skipna, level, ...])	Return the maximum of the values over the requested axis.
	DataFrame.mean([axis, skipna, level, ...])	Return the mean of the values over the requested axis.
	DataFrame.median([axis, skipna, level, ...])	Return the median of the values over the requested axis.
	DataFrame.min([axis, skipna, level, ...])	Return the minimum of the values over the requested axis.
	DataFrame.mode([axis, numeric_only, dropna])	- 선택된 axis의 최빈값을 반환한다.
	DataFrame.pct_change([periods, fill_method, ...])	Percentage change between the current and a prior element.
	DataFrame.prod([axis, skipna, level, ...])	Return the product of the values over the requested axis.
	DataFrame.product([axis, skipna, level, ...])	Return the product of the values over the requested axis.
	DataFrame.quantile([q, axis, numeric_only, ...])	Return values at the given quantile over requested axis.
	DataFrame.rank([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
	DataFrame.round([decimals])	Round a DataFrame to a variable number of decimal places.
	DataFrame.sem([axis, skipna, level, ddof, ...])	Return unbiased standard error of the mean over requested axis.
	DataFrame.skew([axis, skipna, level, ...])	Return unbiased skew over requested axis.
	DataFrame.sum([axis, skipna, level, ...])	Return the sum of the values over the requested axis.
	DataFrame.std([axis, skipna, level, ddof, ...])	Return sample standard deviation over requested axis.
	DataFrame.var([axis, skipna, level, ddof, ...])	Return unbiased variance over requested axis.
	DataFrame.nunique([axis, dropna])	Count number of distinct elements in specified axis.
	DataFrame.value_counts([subset, normalize, ...])	Return a Series containing counts of unique rows in the DataFrame.
Reindexing & Selection & Label Manipulation	DataFrame.add_prefix(prefix)	Prefix labels with string prefix.
	DataFrame.add_suffix(suffix)	Suffix labels with string suffix.
	DataFrame.align(other[, join, axis, level, ...])	Align two objects on their axes with the specified join method.
	DataFrame.at_time(time[, asof, axis])	Select values at particular time of day (e.g., 9:30AM).
	DataFrame.between_time(start_time, end_time)	Select values between particular times of the day (e.g., 9:00-9:30 AM).
	DataFrame.drop([labels, axis, index, ...])	Drop specified labels from rows or columns.
	DataFrame.drop_duplicates([subset, keep, ...])	Return DataFrame with duplicate rows removed.
	DataFrame.duplicated([subset, keep])	Return boolean Series denoting duplicate rows.
	DataFrame.equals(other)	Test whether two objects contain the same elements.
	DataFrame.filter([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.
	DataFrame.first(offset)	Select initial periods of time series data based on a date offset.
	DataFrame.head([n])	Return the first n rows.
	DataFrame.idxmax([axis, skipna])	Return index of first occurrence of maximum over requested axis.
	DataFrame.idxmin([axis, skipna])	Return index of first occurrence of minimum over requested axis.
	DataFrame.last(offset)	Select final periods of time series data based on a date offset.
	DataFrame.reindex([labels, index, columns, ...])	Conform Series/DataFrame to new index with optional filling logic.
	DataFrame.reindex_like(other[, method, ...])	Return an object with matching indices as other object.
	DataFrame.rename([mapper, index, columns, ...])	Alter axes labels.
	DataFrame.rename_axis([mapper, index, ...])	Set the name of the axis for the index or columns.
	DataFrame.reset_index([level, drop, ...])	Reset the index, or a level of it.
	DataFrame.sample([n, frac, replace, ...])	Return a random sample of items from an axis of object.
	DataFrame.set_axis(labels[, axis, inplace])	Assign desired index to given axis.
	DataFrame.set_index(keys[, drop, append, ...])	Set the DataFrame index using existing columns.
	DataFrame.tail([n])	Return the last n rows.
	DataFrame.take(indices[, axis, is_copy])	Return the elements in the given positional indices along an axis.
	DataFrame.truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
Missing Data Handling	DataFrame.backfill([axis, inplace, limit, ...])	Synonym for DataFrame.fillna() with method='bfill'.
	DataFrame.bfill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='bfill'.
	DataFrame.dropna([axis, how, thresh, ...])	Remove missing values.
	DataFrame.ffill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='ffill'.
	DataFrame.fillna([value, method, axis, ...])	Fill NA/NaN values using the specified method.
	DataFrame.interpolate([method, axis, limit, ...])	Fill NaN values using an interpolation method.
	DataFrame.isna()	Detect missing values.
	DataFrame.isnull()	DataFrame.isnull is an alias for DataFrame.isna.
	DataFrame.notna()	Detect existing (non-missing) values.
	DataFrame.notnull()	DataFrame.notnull is an alias for DataFrame.notna.
	DataFrame.pad([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='ffill'.
	DataFrame.replace([to_replace, value, ...])	Replace values given in to_replace with value.
Reshaping & Sorting & Transposing	DataFrame.droplevel(level[, axis])	Return Series/DataFrame with requested index / column level(s) removed.
	DataFrame.pivot([index, columns, values])	Return reshaped DataFrame organized by given index / column values.
	DataFrame.pivot_table([values, index, ...])	Create a spreadsheet-style pivot table as a DataFrame.
	DataFrame.reorder_levels(order[, axis])	Rearrange index levels using input order.
	DataFrame.sort_values(by[, axis, ascending, ...])	Sort by the values along either axis.
	DataFrame.sort_index([axis, level, ...])	Sort object by labels (along an axis).
	DataFrame.nlargest(n, columns[, keep])	Return the first n rows ordered by columns in descending order.
	DataFrame.nsmallest(n, columns[, keep])	Return the first n rows ordered by columns in ascending order.
	DataFrame.swaplevel([i, j, axis])	Swap levels i and j in a MultiIndex.
	DataFrame.stack([level, dropna])	Stack the prescribed level(s) from columns to index.
	DataFrame.unstack([level, fill_value])	Pivot a level of the (necessarily hierarchical) index labels.
	DataFrame.swapaxes(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately.
	DataFrame.melt([id_vars, value_vars, ...])	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
	DataFrame.explode(column[, ignore_index])	Transform each element of a list-like to a row, replicating index values.
	DataFrame.squeeze([axis])	Squeeze 1 dimensional axis objects into scalars.
	DataFrame.to_xarray()	Return an xarray object from the pandas object.
	DataFrame.transpose(*args[, copy])	Transpose index and columns.
Combining & Comparing & Joining & Merging	DataFrame.assign(**kwargs)	Assign new columns to a DataFrame.
	DataFrame.compare(other[, align_axis, ...])	Compare to another DataFrame and show the differences.
	DataFrame.join(other[, on, how, lsuffix, ...])	Join columns of another DataFrame.
	DataFrame.merge(right[, how, on, left_on, ...])	Merge DataFrame or named Series objects with a database-style join.
	DataFrame.update(other[, join, overwrite, ...])	Modify in place using non-NA values from another DataFrame.

 

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Reference: "pandas", pandas.pydata.org, 2022.09.05 검색, URL.