Series

• Pandas | Series constructor
  Pandas Series(~) constructor initialises a new Series.
• Pandas Series | argmax method
  Pandas Series.argmax(~) returns the integer index of the maximum value in the Series. If there are multiple maximums, then the integer index of the first occurrence will be returned.
• Pandas Series | argmin method
  Pandas Series.argmin(~) returns the integer index of the minimum value in the Series. If there are multiple minimums, then the integer index of the first occurrence will be returned.
• Pandas Series | between method
  Pandas Series.between(~) method returns a vector of booleans indicating whether each element in the Series is within the provided range. NaN values are considered as False.
• Pandas Series | map method
  Pandas Series.map(~) method applies a mapping to each value of the Series. The mapping is not applied inplace, that is, a new Series is returned.
• Pandas Series | to_frame method
  Converts a Series to a single-column DataFrame.
• Pandas Series | to_list method
  Pandas Series.to_list(~) method converts the Series into a Python's standard list.
• Pandas Series | value_counts method
  Pandas Series.value_counts(~) method returns the count of unique values in the Series.
• Pandas Series | hasnans property
  Pandas Series.hasnans property returns True if the Series contains a missing value (NaN), and False otherwise.
• Pandas Series | is_monotonic property
  Pandas Series.is_monotonic property returns True if the values in the Series are monotonically increasing, and False otherwise.
• Pandas Series | is_monotonic_decreasing property
  Pandas Series.is_monotonic_decreasing property returns True if the values in the Series are monotonically decreasing, and False otherwise.
• Pandas Series | is_monotonic_increasing property
  Pandas Series.is_monotonic_increasing property returns True if the values in the Series are monotonically increasing, and False otherwise.
• Pandas Series | is_unique property
  Pandas Series.is_unique property returns True if all the values in the Series are unique, otherwise False is returned.

• String Operations

  • Pandas Series str | extract method
    Pandas Series str.extract(~) extracts the first matched substrings using regular expression.
  • Pandas Series str | extractall method
    Pandas Series' str.extractall(~) extracts all the matched substrings using regular expression.
  • Pandas Series str | center method
    Pandas Series.str.center(~) pads the left and right side of each string in the Series until the specified width is reached. If the length of the string is larger than the specified width, then the string is left as is.
  • Pandas Series string | contains method
    Pandas Series str.contains(~) method checks whether or not each value of the source Series contains the specified substring or regex pattern.
  • Pandas Series str | len method
    Pandas Series str.len(~) method returns the length of each string in the Series.
  • Pandas Series str | lower method
    Python's str.lower() method converts all strings in a Series/Index to lowercase.
  • Pandas Series str | lstrip method
    Pandas Series str.lstrip(~) method removes the combination of the specified characters that appear from the left of each string in the Series. Note that a new Series is returned, and the original is kept intact.
  • Pandas Series str | pad method
    Pandas Series.pad(~) pads each string of the Series until the specified length is reached.
  • Pandas Series str | replace method
    Pandas Series str.replace(~) method replaces a substring of each string in the Series with a specified value.
  • Pandas Series str | rstrip method
    Pandas Series str.rstrip(~) method removes the combination of the specified characters that appear from the right of each string in the Series. Note that a new Series is returned, and the original is kept intact.
  • Pandas Series str | split method
    Pandas Series.str.split(~) method performs a split on each string in the Series.
  • Pandas Series str | strip method
    Pandas Series str.strip(~) method removes the combination of the specified characters that appear in the front and back of each string in the Series.
  • Pandas Series | zfill method
    Pandas Series.str.zfill(~) method left-fills each string of the Series with zeros until the specified width is reached.

General Functions

• Pandas | bdate_range method
  Pandas bdate_range(~) method returns a DatetimeIndex containing the specified business days.
• Pandas | concat method
  Pandas concat(~) method concatenates a list of Series or DataFrame, either horizontally or vertically.
• Pandas | crosstab method
  Pandas crosstab(~) method computes the cross tabulation of multiple column variables. By default, the computed statistic is the count.
• Pandas | cut method
  Pandas cut(~) method categorises numerical values into bins (intervals).
• Pandas | date_range method
  Pandas date_range(~) method returns a DatetimeIndex with fixed frequency.
• Pandas | factorize method
  Pandas factorize(~) method returns an array of integer indices to map the input array to the unique values, and all the unique values of the input array.
• Pandas | get_dummies method
  Pandas get_dummies(~) method performs one-hot encoding or dummy coding on categorical variables.
• Pandas | infer_freq method
  Pandas infer_freq(~) method returns a string indicating the inferred frequency of the given DatetimeIndex or a TimedeltaIndex.
• Pandas | interval_range method
  Pandas interval_range(~) method returns an IntervalIndex with fixed frequency.
• Pandas | isnull method
  Pandas isnull(~) method returns a boolean mask where True is set for NaNs (missing values) and False for non-NaNs.
• Python | merge_asof method
  Pandas merge_asof(~) method is used to perform a left join on two DataFrames where the join keys are matched not by equality but by proximity.
• Pandas | merge_ordered method
  Pandas merge_ordered(~) method joins two DataFrames with the option to perform filling or interpolation.
• Pandas | notnull method
  Pandas notnull(~) method returns a boolean mask where True is set for non-NaN values and False for NaNs.
• Pandas | period_range method
  Pandas period_range(~) method returns a PeriodIndex with fixed frequency. A Period represents a specific time span or a duration.
• Pandas | pivot method
  Pandas pivot(~) method converts the input DataFrame into a pivot table.
• Pandas | pivot_table method
  Pandas pivot_table(~) method converts the input DataFrame into a pivot table.
• Pandas | qcut method
  Pandas qcut(~) method categorises numerical values into quantile bins (intervals).
• Pandas | read_csv method
  Pandas read_csv(~) method reads a file, and parses its content into a DataFrame.
• Pandas | to_datetime method
  Pandas to_datetime(~) method converts the argument into a datetime object.
• Pandas | to_numeric method
  Pandas to_numeric(~) method converts the input to a numerical type. By default, either int64 or float64 will be used.
• Pandas | to_timedelta method
  Pandas to_timedelta(~) method converts the given argument into timedeltas.
• Pandas | unique method
  Pandas unique(~) method returns the unique values of the input array. The order of the returned values follow their original order in the input array.
• Pandas DataFrame | wide_to_long method
  Pandas wide_to_long(~) method converts the format of a DataFrame from wide to long.

DataFrame

• Pandas | DataFrame constructor
  Pandas DataFrame(~) constructor is used to initialise a new DataFrame.

• Basic and Descriptive Statistics

  • Pandas DataFrame | abs method
    Pandas DataFrame.abs() method returns a DataFrame with the absolute value applied to each of its values.
  • Pandas DataFrame | clip method
    Pandas DataFrame.clip(~) method is used to ensure that the values of the DataFrame are between a particular range.
  • Pandas DataFrame | corr method
    Pandas DataFrame.corr(~) method computes pair-wise correlation of the columns in the source DataFrame.
  • Pandas DataFrame | corrwith method
    Pandas DataFrame.corrwith(~) computes the pairwise correlation between the columns or rows of the source DataFrame and the given Series or DataFrame.
  • Pandas DataFrame | count method
    Pandas DataFrame.count(~) method counts the number of non-missing values for each row or column of the DataFrame.
  • Pandas DataFrame | cov method
    Pandas DataFrame.cov(~) method computes the covariance matrix of the columns in the source DataFrame.
  • Pandas DataFrame | cummax method
    Pandas DataFrame.cummax(~) method computes the cumulative maximum along the row or column of the source DataFrame.
  • Pandas DataFrame | cummin method
    Pandas DataFrame.cummin(~) method computes the cumulative minimum along the row or column of the source DataFrame.
  • Pandas DataFrame | cumprod method
    Pandas DataFrame.cumprod(~) method computes the cumulative product along the row or column of the source DataFrame.
  • Pandas DataFrame | cumsum method
    Pandas DataFrame.cumsum(~) method computes the cumulative sum along the row or column of the source DataFrame.
  • Pandas DataFrame | describe method
    Pandas DataFrame.describe(~) method returns a DataFrame containing some descriptive statistics (e.g. mean and min) of the columns of the source DataFrame. This is most commonly used to numerically summarise a given dataset.
  • Pandas DataFrame | diff method
    Pandas DataFrame.diff(~) method returns a new DataFrame where each value represents the difference between the value and the value of the previous row or column.
  • Pandas DataFrame | eval method
    Pandas DataFrame.eval(~) method evaluates an operation, which is encoded as a string, on the DataFrame's columns.
  • Pandas DataFrame | mad method
    Pandas DataFrame.mad(~) method computes the mean absolute deviation (MAD) for each row or column of the DataFrame.
  • Pandas DataFrame | max method
    Pandas DataFrame.max(~) method computes the maximum of each column or row of the DataFrame.
  • Pandas DataFrame | mean method
    Pandas DataFrame.mean(~) method computes the mean for each row or column of the DataFrame.
  • Pandas DataFrame | median method
    Pandas DataFrame.median(~) method computes the median for each row or column of the DataFrame.
  • Pandas DataFrame | min method
    Pandas DataFrame.min(~) method computes the minimum of each row or column of the DataFrame.
  • Pandas DataFrame | mode method
    Pandas DataFrame.mode(~) method computes the mode of each column or row of the DataFrame. If the counts of some values are the same, then all their modes will be returned.
  • Pandas DataFrame | nunique method
    Pandas DataFrame.nunique(~) method counts the number of unique values for each row or column.
  • Pandas DataFrame | pct_change method
    Pandas DataFrame.pct_change(~) computes the percentage change between consecutive values of each column of the DataFrame.
  • Pandas DataFrame | product method
    Pandas DataFrame.product(~) method computes the product for each row or column of the DataFrame.
  • Pandas DataFrame | quantile method
    Pandas DataFrame.quantile(~) method returns the interpolated value at the specified quantile.
  • Pandas DataFrame | rank method
    Pandas DataFrame.rank(~) method computes the ordering of the values for each row or column of the DataFrame.
  • Pandas DataFrame | round method
    Pandas DataFrame.round(~) method returns a DataFrame with all its numerical values rounded according to the specified parameters.
  • Pandas DataFrame | sem method
    Pandas DataFrame.sem(~) method returns the unbiased standard error of the mean for each row or column of the source DataFrame.
  • Pandas DataFrame | std method
    Pandas DataFrame.std(~) method computes the standard deviation of each row or column of the source DataFrame.
  • Pandas DataFrame | sum method
    Pandas DataFrame.sum(~) method computes the sum for each row or column of the source DataFrame.
  • Pandas DataFrame | var method
    Pandas DataFrame.var(~) method computes the variance of each row or column of the source DataFrame.

• Binary Operators

  • Pandas DataFrame | add method
    Pandas DataFrame.add(~) method computes the sum of the values in the source DataFrame and another scalar, sequence Series or DataFrame.
  • Pandas DataFrame | combine method
    Pandas DataFrame.combine(~) method combines the columns of two DataFrames. Note that only columns that share the same column label will be combined.
  • Pandas DataFrame | combine_first method
    Pandas DataFrame.combine_first(~) method replaces all instances of missing values (NaN) in one DataFrame with non-missing values from the other DataFrame.
  • Pandas DataFrame | div method
    Pandas DataFrame.div(~) method divides the values in the source DataFrame by a scalar, sequence, Series or DataFrame.
  • Pandas DataFrame | dot method
    Pandas DataFrame.dot(~) method is used to compute matrix-vector and matrix-matrix multiplication.
  • Pandas DataFrame | eq method
    Pandas DataFrame.eq(~) method returns a DataFrame of booleans where True indicates an entry that is equal to the specified value.
  • Pandas DataFrame | floordiv method
    Pandas DataFrame.floordiv(~) method divides the values in the source DataFrame by a scalar, sequence, Series or DataFrame via integer division.
  • Pandas DataFrame | ge method
    Pandas DataFrame.ge(~) method returns a DataFrame of booleans where True indicates an entry that is greater than or equal to the specified value.
  • Pandas DataFrame | gt method
    Pandas DataFrame.gt(~) method returns a DataFrame of booleans where True indicates an entry that is strictly greater than the specified value.
  • Pandas DataFrame | le method
    Pandas DataFrame.le(~) method returns a DataFrame of booleans where True indicates an entry that is less than or equal to the specified value.
  • Pandas DataFrame | lt method
    Pandas DataFrame.lt(~) method returns a DataFrame of booleans where True indicates an entry that is strictly less than the specified value.
  • Pandas DataFrame | mod method
    Pandas DataFrame.mod(~) method computes the modulo of the values in the source DataFrame and another scalar, sequence, Series or DataFrame.
  • Pandas DataFrame | mul method
    Pandas DataFrame.mul(~) method multiplies the values in the source DataFrame to a scalar, sequence, Series or DataFrame.
  • Pandas DataFrame | ne method
    Pandas DataFrame.ne(~) method returns a DataFrame of booleans where True indicates an entry that is not equal (!=) to the specified value.
  • Pandas DataFrame | pow method
    Pandas DataFrame.pow(~) method computes the exponential power of the values in the source DataFrame and another scalar, sequence, Series or DataFrame.
  • Pandas DataFrame | radd method
    Pandas DataFrame.radd(~) method computes and returns the sum of a scalar, sequence, Series or DataFrame and the values in the source DataFrame.
  • Pandas DataFrame | rdiv method
    Pandas DataFrame.rdiv(~) method divides a scalar, sequence, Series or DataFrame by the values in the source DataFrame.
  • Pandas DataFrame | rfloordiv method
    Pandas DataFrame.rfloordiv(~) method performs integer division between a scalar, sequence, Series or DataFrame and the values in the source DataFrame.
  • Pandas DataFrame | rmod method
    Pandas DataFrame.rmod(~) method computes the modulo of the values in the source DataFrame and another scalar, sequence, Series or DataFrame.
  • Pandas DataFrame | rmul method
    Pandas DataFrame.rmul(~) method multiplies a scalar, sequence, Series or DataFrame to the values in the source DataFrame.
  • Pandas DataFrame | rpow method
    Pandas DataFrame.rpow(~) method computes the exponential power of a scalar, sequence, Series or DataFrame and the values in the source DataFrame.
  • Pandas DataFrame | rsub method
    Pandas DataFrame.rsub(~) method subtracts the values in the source DataFrame from a scalar, sequence, Series or DataFrame.
  • Pandas DataFrame | rtruediv method
    Pandas DataFrame.rtruediv(~) method divides a scalar, sequence, Series or DataFrame by the values in the source DataFrame.
  • Pandas DataFrame | sub method
    Pandas DataFrame.sub(~) method subtracts a scalar, sequence, Series or DataFrame from the values in the source DataFrame.
  • Pandas DataFrame | truediv method
    Pandas DataFrame.truediv(~) method divides the values in the source DataFrame by a scalar, sequence, Series or DataFrame.

• Combining DataFrames

  • Pandas DataFrame | append method
    Pandas DataFrame.append(~) method appends new rows to the source DataFrame.
  • Pandas DataFrame | assign method
    Pandas DataFrame.assign(~) method appends a new column to the DataFrame.
  • Pandas DataFrame | insert method
    Pandas DataFrame.insert(~) method inserts a new column into the source DataFrame.
  • Pandas DataFrame | join method
    Pandas DataFrame.join(~) merges the source DataFrame with other Series or DataFrames.
  • Pandas DataFrame | merge method
    Pandas DataFrame.merge(~) method merges the source DataFrame with another DataFrame or a named Series.
  • Pandas DataFrame | pop method
    Pandas DataFrame.pop(~) method removes a single column of a DataFrame.
  • Pandas DataFrame | update method
    Pandas DataFrame.update(~) method replaces the values in the source DataFrame using non-NaN values from another DataFrame.

• Data Indexing and Masks

  • Pandas DataFrame | all method
    Pandas DataFrame.all(~) method checks each row or column, and returns True for that row/column if all its values evaluate to True.
  • Pandas DataFrame | any method
    Pandas any(~) method scans through each row or column of the DataFrame, and if the row/column contains at least one non-zero value or a boolean True, then True is returned for that row/column.
  • Pandas DataFrame | isin method
    Pandas's DataFrame.isin(~) method checks if certain values are present in the DataFrame.
  • Pandas DataFrame | isna method
    Pandas's DataFrame.isna(~) method returns a boolean mask where True is set for missing values (NaN) and False for non-missing values.
  • Pandas DataFrame | isnull method
    Pandas DataFrame.isnull(~) method returns a boolean mask where True is set for missing values (NaNs) and False for the rest.
  • Pandas DataFrame | mask method
    Pandas's DataFrame.mask(~) replaces all values in the DataFrame that pass a certain criteria with the desired value.
  • Pandas DataFrame | notna method
    Pandas's DataFrame.notna(~) method returns a boolean mask where True is set for non-missing values and False for NaNs.
  • Pandas DataFrame | where method
    Pandas DataFrame.where(~) uses a boolean mask to selectively replace values in the source DataFrame.
  • Pandas DataFrame | at property
    Panda's DataFrame.at property is used to access or update a single value in the DataFrame using row/column labels.
  • Pandas DataFrame | iat property
    Panda's DataFrame.iat property is used to access or update a single value in the DataFrame using integer syntax.
  • Pandas DataFrame | iloc property
    Pandas's DataFrame.iloc is used to access and update specific rows/columns of the DataFrame using integer indices.
  • Pandas DataFrame | loc property
    Pandas DataFrame.loc is used to access and update values of the DataFrame using row and column labels.

• Data Selection and Renaming

  • Pandas DataFrame | add_prefix method
    Pandas DataFrame.add_prefix(~) prepends the specified string to each column label of the DataFrame.
  • Pandas DataFrame | add_suffix method
    Pandas's DataFrame.add_suffix(~) appends the specified string to each column label of the DataFrame.
  • Pandas DataFrame | align method
    Pandas DataFrame.align(~) method ensures that two DataFrames have the same column or row labels.
  • Pandas DataFrame | at_time method
    Pandas DataFrame.at_time(~) method extracts rows that are of the specified time of day.
  • Pandas DataFrame | between_time method
    Pandas DataFrame.between_time(~) method returns rows that fall between the specified start time and end time.
  • Pandas DataFrame | drop method
    Pandas's DataFrame.drop(~) method returns a DataFrame with the specified columns/rows removed.
  • Pandas DataFrame | drop_duplicates method
    Pandas's DataFrame.drop_duplicates(~) method returns a DataFrame with duplicate rows removed.
  • Pandas DataFrame | duplicated method
    Pandas DataFrame.duplicated(~) method returns a Series of booleans where True represents duplicate rows.
  • Pandas DataFrame | equals method
    Pandas DataFrame.equals(~) checks whether two DataFrames are identical, that is, all their respective values, column labels and index names are equal, and have the same data type.
  • Pandas DataFrame | filter method
    Pandas DataFrame.filter(~) method returns the rows or columns where the label matches the specified pattern.
  • Pandas DataFrame | first method
    Pandas DataFrame.first(~) method returns all rows that fall in the first n days (or whatever time unit you specify), where n is the specified offset.
  • Pandas DataFrame | get method
    Pandas's DataFrame.get(~) is used to access columns of the DataFrame.
  • Pandas DataFrame | head method
    Pandas DataFrame.head(~) method returns the first n rows of the DataFrame.
  • Pandas DataFrame | idxmax method
    Pandas DataFrame.idxmax(~) method returns the column/row label of the maximum value of each row/column of the source DataFrame.
  • Pandas DataFrame | idxmin method
    Pandas DataFrame.idxmin(~) method returns the column/row label of the minimum value of each row/column of the source DataFrame.
  • Pandas DataFrame | last method
    Pandas DataFrame.last(~) method returns all rows that fall in the last n days (or whatever time unit you specify), where n is the specified offset.
  • Pandas DataFrame | lookup method
    Pandas DataFrame.lookup(~) method extracts individual values from the source DataFrame in a single Numpy Array.
  • Pandas DataFrame | query method
    Pandas's DataFrame.query(~) method filters rows according to the provided boolean expression.
  • Pandas DataFrame | reindex method
    Pandas DataFrame.reindex(~) method sets a new index for the source DataFrame, and places NaN to values whose row or column label is new.
  • Pandas DataFrame | rename method
    Panda's DataFrame.rename(~) method renames the columns and indexes of the DataFrame.
  • Pandas DataFrame | rename_axis method
    Pandas's DataFrame.rename_axis(~) method modifies the axis labels.
  • Pandas DataFrame | reset_index method
    Pandas's DataFrame.reset_index(~) resets the index to the default integer index.
  • Pandas DataFrame | sample method
    Pandas DataFrame.sample(~) method returns the specified number of rows or columns randomly.
  • Pandas DataFrame | select_dtypes method
    Pandas DataFrame.select_dtypes(~) returns a subset of columns that match (or not match) the specified type.
  • Pandas DataFrame | set_axis method
    Pandas's DataFrame.set_axis(~) method assigns a new label to the source DataFrame's columns or index.
  • Pandas DataFrame | set_index method
    Pandas's DataFrame.set_index(~) sets the index of the DataFrame using one of its columns.
  • Pandas DataFrame | tail method
    Pandas DataFrame.tail(~) method returns the last n rows of the DataFrame.
  • Pandas DataFrame | take method
    Pandas DataFrame.take(~) method returns a new DataFrame containing the specified rows or columns.
  • Pandas DataFrame | truncate method
    Panda's DataFrame.truncate(~) method extracts a subset of rows from the DataFrame using truncation.

• Functions and Aggregations

  • Pandas DataFrame | agg method
    Pandas DataFrame.agg(~) applies the specified function to each row or column of the DataFrame.
  • Pandas DataFrame | apply method
    Pandas DataFrame.apply(~) applies the specified function to each row or column of the DataFrame.
  • Pandas DataFrame | applymap method
    Pandas DataFrame.applymap(~) method applies a function on each entry of the source DataFrame. Note that a new copy of the DataFrame will be returned, and so the source DataFrame will be kept intact.
  • Pandas DataFrame | expanding method
    Pandas DataFrame.expanding(~) method is used to compute cumulative statistics.
  • Pandas DataFrame | groupby method
    Pandas's DataFrame.groupby(~) divides up your DataFrame into groups based on the specified criteria. The returned value is useful because it allows you to compute statistics (e.g. mean and min) and apply transformations group-wise.
  • Pandas DataFrame | pipe method
    Pandas DataFrame.pipe(~) method applies a specified function on the source DataFrame.
  • Pandas DataFrame | rolling method
    Pandas DataFrame.rolling(~) method is used to compute the statistics using moving windows. Note that a window is simply a sequence of values used to compute a statistics like the mean.
  • Pandas DataFrame | transform method
    Pandas DataFrame.transform(~) method applies a function to transform the rows or columns of the source DataFrame.

• Handling Missing Values

  • Pandas DataFrame | dropna method
    Pandas's DataFrame.dropna(~) method removes rows or columns with missing values (NaNs).
  • Pandas DataFrame | fillna method
    Pandas DataFrame.fillna(~) method fills missing values (NaNs) with a specified value or with a filling rule.
  • Pandas DataFrame | interpolate method
    Pandas DataFrame.interpolate(~) method fills missing values (NaNs) using interpolated values.
  • Pandas DataFrame | replace method
    Pandas's DataFrame.replace(~) method replaces the specified values with another set of values.

• Iterators

  • Pandas DataFrame | __iter__ method
    Pandas DataFrame.__iter__() method returns an iterator over the column labels.
  • Pandas DataFrame | items method
    Pandas's DataFrame.items() method is used to iterate over all pairs of column name (string) and column values (Series) in the DataFrame.
  • Pandas DataFrame | iteritems method
    Pandas DataFrame.iteritems() method is used to iterate over the column labels and column data of the source DataFrame.
  • Pandas DataFrame | iterrows method
    Pandas's DataFrame.iterrows() method is used to iterate over all pairs of row name (object) and row values (Series) in the source DataFrame.
  • Pandas DataFrame | itertuples method
    Panda's DataFrame.itertuples(~) method is used to iterate over the rows of the DataFrame as named tuples.
  • Pandas DataFrame | keys method
    Pandas DataFrame.keys() method returns the columns of the DataFrame as an Index object.

• Meta Information

  • Pandas DataFrame | info method
    Pandas DataFrame.info(~) method outputs a brief summary of the DataFrame, which includes information such as the data-types and memory consumption.
  • Pandas DataFrame | memory_usage method
    Pandas DataFrame.memory_usage(~) returns the amount of memory each column occupies in bytes.

• Miscellaneous

  • Pandas DataFrame | bool method
    Pandas DataFrame.bool() method returns True if a DataFrame of size one holds a True boolean, and False if it holds a False boolean.
  • Pandas DataFrame | copy method
    Pandas DataFrame.copy(~) method makes a copy of a DataFrame. You can choose whether you want a deep copy or a shallow copy.

• Properties

  • Pandas DataFrame | T property
    Pandas DataFrame.T property transposes the rows and columns.
  • Pandas DataFrame | axes property
    Pandas DataFrame.axes property returns the row and column labels of the DataFrame as a list.
  • Pandas DataFrame | columns property
    Panda's DataFrame.columns property returns an Index containing the column names of the DataFrame.
  • Pandas DataFrame | dtypes property
    Pandas DataFrame.dtypes property returns the data type of the values stored in each column.
  • Pandas DataFrame | empty property
    Pandas DataFrame.empty property returns True if the DataFrame does not contain any elements.
  • Pandas DataFrame | index property
    Panda's DataFrame.index property returns an Index containing the index names of the DataFrame.
  • Pandas DataFrame | ndim property
    Pandas DataFrame.ndim property returns the number of dimensions of the DataFrame, which is always 2.
  • Pandas DataFrame | shape property
    Pandas DataFrame.shape property returns the number of rows and columns in the DataFrame as a tuple.
  • Pandas DataFrame | size property
    Pandas's DataFrame.size property returns the number of values in the DataFrame.
  • Pandas DataFrame | values property
    Pandas DataFrame.values property returns the values of the DataFrame as a 2D NumPy array.

• Sorting and Restructuring DataFrames

  • Pandas DataFrame | droplevel method
    Pandas DataFrame.droplevel(~) method returns a new DataFrame with the specified column or index levels removed.
  • Pandas DataFrame | explode method
    Pandas DataFrame.explode(~) method unwraps array-likes such as lists, tuples, Series and Numpy arrays vertically.
  • Pandas DataFrame | melt method
    Pandas DataFrame.melt(~) method converts the format of the source DataFrame from "wide" to "long".
  • Pandas DataFrame | nlargest method
    Pandas DataFrame.nlargest(~) method returns n rows with the largest value in the specified columns.
  • Pandas DataFrame | nsmallest method
    Pandas DataFrame.nsmallest(~) method returns n rows with the smallest value for the specified columns.
  • Pandas DataFrame | reorder_levels method
    Pandas DataFrame.reorder_levels(~) method changes the ordering of levels.
  • Pandas DataFrame | sort_index method
    Pandas's DataFrame.sort_index(~) method sorts the source DataFrame by either column or index labels.
  • Pandas DataFrame | sort_values method
    Pandas DataFrame.sort_values(~) method sorts the source DataFrame either by column or row values.
  • Pandas DataFrame | squeeze method
    Pandas DataFrame.squeeze(~) method reduces a DataFrame with a single row or column to a Series.
  • Pandas DataFrame | stack method
    Pandas DataFrame.stack(~) method converts the specified column levels to row levels.
  • Pandas DataFrame | swapaxes method
    Pandas DataFrame.swapaxes(~) method swaps the rows and the columns of the DataFrame.
  • Pandas DataFrame | swaplevel method
    Pandas DataFrame.swaplevel(~) method swaps two levels of a multi-index DataFrame.
  • Pandas DataFrame | transpose method
    Pandas DataFrame.transpose(~) method swaps the rows and columns of the source DataFrame.
  • Pandas DataFrame | unstack method
    Pandas DataFrame.unstack(~) method converts the specified row levels to column levels.

• Time Series

  • Pandas DataFrame | asfreq method
    Pandas DataFrame.asfreq(~) method returns a DataFrame with a new time index that respects the specified frequency.
  • Pandas DataFrame | asof method
    Pandas DataFrame.asof(~) method returns the last row that contains no NaN.
  • Pandas DataFrame | first_valid_index method
    Pandas DataFrame.first_valid_index(~) method returns the index of the first occurrence of a row containing a non-NaN value. The check is performed row-wise, starting from the first row.
  • Pandas DataFrame | last_valid_index method
    Pandas DataFrame.last_valid_index(~) method returns the index of the last occurrence of a row containing a non-NaN value. The check is performed row-wise, starting from the first row.
  • Pandas DataFrame | resample method
    Pandas DataFrame.resample(~) method performs a group-by based on time.
  • Pandas DataFrame | shift method
    Pandas DataFrame.shift(~) method shifts the rows or columns by the specified amount, without changing the size of the DataFrame.
  • Pandas DataFrame | slice_shift method
    Pandas DataFrame.slice_shift(~) method shifts the rows of the DataFrame by the specified amount, and empty rows are stripped away.
  • Pandas DataFrame | to_period method
    Pandas DataFrame.to_period(~) method converts the source DataFrame's DatetimeIndex to PeriodIndex.
  • Pandas DataFrame | to_timestamp method
    Pandas DataFrame.to_timestamp(~) method converts the row index or column index to DatetimeIndex.
  • Pandas DataFrame | tshift method
    Pandas DataFrame.tshift(~) method shifts the date index of the source DataFrame.
  • Pandas DataFrame | tz_convert method
    Pandas DataFrame.tz_convert(~) method converts the timezone of the row or column index.
  • Pandas DataFrame | tz_localize method
    Pandas DataFrame.tz_localize(~) method makes the DataFrame's index timezone-aware.

• Type Conversion

  • Pandas DataFrame | astype method
    Pandas DataFrame.astype(~) method converts the data type of the columns of a DataFrame to the specified type.
  • Pandas DataFrame | convert_dtypes method
    Pandas DataFrame.convert_dtypes(~) method converts the data type of the columns of the source DataFrame to a more suitable/specific one.
  • Pandas DataFrame | infer_objects method
    Pandas DataFrame.infer_objects(~) method converts columns that are of data-type object to a more specific type by inferring from its data.
  • Pandas DataFrame | to_csv method
    Pandas DataFrame.to_csv(~) method converts the source DataFrame into comma-separated value format.
  • Pandas DataFrame | to_dict method
    Pandas DataFrame.to_dict(~) method converts the DataFrame to a Python dictionary or list of dictionaries.
  • Pandas DataFrame | to_json method
    Pandas DataFrame.to_json(~) method either converts a DataFrame to a JSON string, or outputs a JSON file.
  • Pandas DataFrame | to_numpy method
    Pandas DataFrame.to_numpy(~) method returns the values of the DataFrame as a 2D Numpy array.

Datetimes

• Pandas | DatetimeIndex constructor
  Pandas DatetimeIndex constructor creates a new DatetimeIndex object, which is most often used as the index of a DataFrame.
• Pandas | Period constructor
  Pandas Period(~) constructor creates a new Period object, which represents a specific time span or a duration.
• Pandas | PeriodIndex constructor
  Pandas PeriodIndex(~) constructor creates a new PeriodIndex object, which represents a specific time span or a duration. PeriodIndex is most often used as the index of a DataFrame.
• Pandas | Timedelta constructor
  Pandas Timedelta(~) constructor creates a new Timedelta object, which represents a time difference in units that you specify. It is typically used to perform date arithmetics.
• Pandas | Timestamp constructor
  Timestamp is the Pandas' implementation of Python's Datetime, and their functionality and properties are nearly identical. Timestamp is the underlying data structure used for DatetimeIndex.