DataFrame Basic Functions

Here intoduces some basic functions of DataFrame.

Constructor

DataFrame([data, index, columns, dtype, copy]) #Construct data frame

DataFrame.axes #index: row labels; columns: column labels
DataFrame.as_matrix([columns]) #Convert to matrix
DataFrame.dtypes #Return data types
DataFrame.ftypes #Return the data type float64:dense of each column
DataFrame.get_dtype_counts() #Return the number of data types in the data frame
DataFrame.get_ftype_counts() #Return the data frame data type float64: the number of dense
DataFrame.select_dtypes([include, include]) #Select sub-data frame according to data type
DataFrame.values #Numpy display method
DataFrame.axes #Return the label name of the horizontal and vertical coordinates
DataFrame.ndim #Return the latitude of the data frame
DataFrame.size #Return the number of data frame elements
DataFrame.shape #Return the shape of the data frame
DataFrame.memory_usage() #Storage of each column

DataFrame.astype(dtype[, copy, errors]) #Convert data type
DataFrame.copy([deep]) #deep deep copy data
DataFrame.isnull() #return null value in Boolean way
DataFrame.notnull() #return non-null value in Boolean way

DataFrame.head([n]) #Return the first n rows of data
DataFrame.at #Quick label constant accessor
DataFrame.iat #Fast integer constant accessor
DataFrame.loc #label positioning, using the name
DataFrame.iloc #Integer positioning, using numbers
DataFrame.insert(loc, column, value) #In a special place loc[number] insert column[column name] a column of data
DataFrame.iter() #Iterate over infor axis
DataFrame.iteritems() #Iterator that returns column names and sequences
DataFrame.iterrows() #Iterator returning index and sequence
DataFrame.itertuples([index, name]) #Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple.
DataFrame.lookup(row_labels, col_labels) #Label-based “fancy indexing” function for DataFrame.
DataFrame.pop(item) #Return deleted items
DataFrame.tail([n]) #Return the last n rows
DataFrame.xs(key[, axis, level, drop_level]) #Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
DataFrame.isin(values) #Whether contains elements in the data frame
DataFrame.where(cond[, other, inplace, …]) #conditional filter
DataFrame.mask(cond[, other, inplace, …]) #Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.
DataFrame.query(expr[, inplace]) #Query the columns of a frame with a boolean expression.

DataFrame.add(other[,axis,fill_value]) #Addition, element pointing
DataFrame.sub(other[,axis,fill_value]) #Subtraction, element pointing
DataFrame.mul(other[, axis,fill_value]) #Multiplication, element pointing
DataFrame.div(other[, axis,fill_value]) #decimal division, element pointing
DataFrame.truediv(other[, axis, level, …]) #True division, element pointing
DataFrame.floordiv(other[, axis, level, …]) #Fall down to divide, element points
DataFrame.mod(other[, axis,fill_value]) #modulo operation, element pointing
DataFrame.pow(other[, axis,fill_value]) #Power operation, element pointing
DataFrame.radd(other[, axis,fill_value]) #Addition on the right, the element points to
DataFrame.rsub(other[, axis,fill_value]) #Subtraction on the right, element pointing
DataFrame.rmul(other[, axis,fill_value]) #right side multiplication, element pointing
DataFrame.rdiv(other[, axis,fill_value]) #Right decimal division on the right, element pointing
DataFrame.rtruediv(other[, axis, …]) #True division on the right, element pointing
DataFrame.rfloordiv(other[, axis, …]) #The right side is rounded down, the element points
DataFrame.rmod(other[, axis,fill_value]) #Modular operation on the right, element pointing
DataFrame.rpow(other[, axis,fill_value]) #right power operation, element pointing
DataFrame.lt(other[, axis, level]) #Similar to Array.lt
DataFrame.gt(other[, axis, level]) #Similar to Array.gt
DataFrame.le(other[, axis, level]) #Similar to Array.le
DataFrame.ge(other[, axis, level]) #Similar to Array.ge
DataFrame.ne(other[, axis, level]) #Similar to Array.ne
DataFrame.eq(other[, axis, level]) #Similar to Array.eq
DataFrame.combine(other,func[,fill_value, …]) #Add two DataFrame objects and do not propagate NaN values, so if for a
DataFrame.combine_first(other) #Combine two DataFrame objects and default to non-null values in frame calling the method.

DataFrame.apply(func[, axis, broadcast, …]) #application function
DataFrame.applymap(func) #Apply a function to a DataFrame that is intended to operate elementwise, i.e.
DataFrame.aggregate(func[, axis]) #Aggregate using callable, string, dict, or list of string/callables
DataFrame.transform(func, *args, **kwargs) #Call function producing a like-indexed NDFrame
DataFrame.groupby([by, axis, level, …]) #Group
DataFrame.rolling(window[, min_periods, …]) #rolling window
DataFrame.expanding([min_periods, freq, …]) #Expand the window
DataFrame.ewm([com, span, halflife, …]) #exponential weight window