Pandas objects
Series |
a one-dimensional labeled array that can hold any data type |
DataFrame |
a two-dimensional labeled data structure with columns of potentially different types |
Index |
a sequence of axis labels that can be used to identify rows or columns in a DataFrame |
MultiIndex |
a hierarchical index object that allows for more than one index level in a DataFrame. |
Timestamp |
a specific moment in time, represented in Pandas as a datetime object. |
Period |
a specific interval of time, represented in Pandas as a period object. |
DatetimeIndex |
an index of datetime objects, used to index Pandas objects like Series and DataFrame. |
Timedelta |
a duration of time, represented in Pandas as a timedelta object. |
Categorical |
a data type used to represent categorical variables, |
Sparse |
a data structure used to represent sparse data efficiently, |
Interval |
a data type used to represent intervals |
DatetimeTZ |
a datetime object with a timezone. |
Pandas functions called on pd
pd.read_csv() |
used to read data from a CSV file and create a DataFrame. |
pd.DataFrame() |
a constructor function that is used to create a new DataFrame from data in memory. |
pd.Series(): |
used to create a new Series object. |
pd.concat() |
used to concatenate two or more DataFrames or Series |
pd.merge(): |
used to merge two DataFrames based on a common column. |
pd.groupby() |
used to group data in a DataFrame based on one or more columns. |
pd.pivot_table(): |
used to create a pivot table from a DataFrame. |
pd.to_datetime(): |
used to convert a column of strings to datetime objects. |
pd.to_numeric() |
used to convert a column of strings to numeric objects. |
pd.cut() |
This function is used to bin data into discrete intervals. |
pd.qcut(): |
This function is used to bin data into quantiles. |
pd.date_range() |
used to create a range of dates or DatetimeIndex. |
pd.timedelta() |
used to create a timedelta object representing a duration of time. |
String vectorization
str.replace(): |
used to replace a pattern in a string with another pattern |
str.extract() |
used to extract a pattern from a string and return the first match. |
str.split() |
used to split a string into a list of strings based on a delimiter. |
str.join() |
used to concatenate a list of strings with a separator string |
str.startswith() |
used to check if a string starts with a particular substring |
str.endswith() |
used to check if a string ends with a particular substring |
str.lower(), str.upper(): |
used to convert the case of a string to lowercase or uppercase, respectively |
str.strip(): |
used to remove leading and trailing whitespace from a string |
str.contains() |
used to check if a pattern exists in a string |
Date Vectorization
pd.to_datetime(): |
used to convert a column of strings or Unix timestamps to a datetime object. |
dt.date |
used to extract the date component of a datetime object or DatetimeIndex. |
dt.time |
extract the time component of a datetime object or DatetimeIndex. |
dt.hour, dt.minute, dt.second |
used to extract the hour, minute, and second components of a datetime object or DatetimeIndex. |
dt.dayofweek: |
used to extract the day of the week as an integer, |
dt.day_name() |
used to extract the name of the day of the week |
dt.month_name() |
used to extract the name of the month |
dt.tz_localize() and dt.tz_convert(): |
used to set or convert the timezone of a datetime object or DatetimeIndex. |
dt.is_month_start and dt.is_month_end |
used to check if a datetime object or DatetimeIndex is at the start or end of a month, respectively. |
pandas groupby
Consider it as DataFrame; you can use all DataFrame attributes and functions on the group object. |
Pandas pivot_table
data |
The DataFrame or Series to be used for the pivot table. |
values |
The column to aggregate. If not specified, all numerical columns will be used. |
index |
The column(s) to use as row labels. |
columns |
The column(s) to use as column labels. |
aggfunc |
The function to use for aggregating the values. Defaults to 'mean'. |
margins |
Add all row/columns (e.g. 'All') label and compute grand total for that row/column. |
margins_name |
The label for the row/column that contains the grand total. Defaults to 'All'. |
dropna |
Whether or not to exclude rows with missing values. Defaults to True. |
sort |
Whether or not to sort the rows by the values of the index. |
Pandas multiIndex
# Create a MultiIndex from two separate indexes
index1 = pd.Index(['A', 'B', 'C'], name='Index1')
index2 = pd.Index(['X', 'Y', 'Z'], name='Index2')
multi_index = pd.MultiIndex.from_product([index1, index2], names=['Index1', 'Index2'])
# Create a MultiIndex from a list of tuples
tuples = [('A', 'X'), ('A', 'Y'), ('B', 'X'), ('B', 'Y')]
multi_index = pd.MultiIndex.from_tuples(tuples, names=['Index1', 'Index2'])
# Select data from a single level of the index
df.loc['A'] # selects all rows where Index1 = 'A'
df.loc[('A', 'X')] # selects a single row where Index1 = 'A' and Index2 = 'X'
# Select data from multiple levels of the index
# selects rows where Index1 is 'A' or 'B' and Index2 is 'X', and returns the 'Column1' values
df.loc[(['A', 'B'], 'X'), 'Column1']
# Stack a DataFrame to move a level of the MultiIndex to the columns
df.stack()
# Unstack a DataFrame to move a level of the columns to the index
df.unstack()
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