Cheatography
https://cheatography.com
Creating this cheatsheet.
This is a draft cheat sheet. It is a work in progress and is not finished yet.
Creating Series
From a list: |
series = pd.Series(my_list) |
From a dictionary: |
series = pd.Series(my_dict) |
From a NumPy array: |
series = pd.Series(my_array) |
Constructor
data |
used to pass the data for the Series. |
index |
used to specify the index labels for the Series |
dtype |
used to specify the data type of the Series |
copy |
used to specify whether to make a copy of the data or not |
name |
used to give a name to the Series |
example: |
pd.Series(data=my_data, index=my_index) |
Series Attributes
values |
Returns a NumPy array containing the values of the Series |
index |
Returns the index labels of the Series |
shape |
Returns the dimensions of the Series in the form of a tuple. |
size |
Returns the number of elements in the Series. |
name |
Returns the name of the Series. |
dtype |
Returns the data type of the values in the Series. |
is_unique |
Boolean value indicating whether all the values in the Series are unique or not. |
Data Selection
head() |
Return the first n rows of the Series (default value of n is 5). |
tail() |
Return the last n rows of the Series (default value of n is 5). |
sample() |
Return a random sample of the Series. |
value_counts() |
Count the number of occurrences of each unique value in the Series. |
Sorting
sort_values() |
Sort the Series by value. |
sort_index() |
Sort the Series by index. |
Math
count() |
Return the number of non-missing values in the Series. |
sum() |
Return the sum of the values in the Series. |
mean() |
Return the mean of the values in the Series. |
min() |
Return the minimum value in the Series. |
max() |
Return the maximum value in the Series. |
describe() |
Generate descriptive statistics for the Series. |
Python Functions
# len/type/dir/sorted/max/min
type(students_series)
# type conversion
list(marks_series)
# Arithmetic Operators(Broadcasting)
100 + marks_series
|
Indexing
Fancy Indexing |
students[[1,3,4,5]] , students[['A','B']] |
Boolean Indexing |
stduent_mark[student_mark >= 50] |
Normal Indexing |
student[1] , students[1:2] , students[::2] |
Error |
students[[1:2]] , student[-1] |
Plotting
plot() |
Creates a line plot of the Series. |
bar() |
Creates a vertical bar plot of the Series. |
hist() |
Creates a histogram plot of the Series. |
scatter() |
Creates a scatter plot of the Series. |
Plot()
kind |
The type of plot to create, e.g., 'line', 'bar', 'hist', 'kde', 'pie', etc. Defaults to 'line' |
title |
The title of the plot. |
xlabel |
The label for the x-axis |
ylabel |
The label for the y-axis. |
color |
The color of the plot. |
legend |
Whether or not to show a legend. Defaults to True. |
figsize |
The size of the plot figure in inches. |
grid |
Whether or not to show a grid. Defaults to True. |
xticks |
he positions of the x-axis ticks. |
yticks |
Additional parameters for the specific plot |
|
|
Data Manipulation:
astype() |
Convert the data type of the values in the Series to a specified data type. |
between() |
Return a Boolean Series indicating whether each element is between two values (inclusive). |
clip() |
Limit the values in the Series to a specified range. |
drop_duplicates() |
Remove duplicate values from the Series. |
dropna() |
Remove missing values from the Series. |
fillna() |
Fill missing values in the Series with a specified value or method. |
isin() |
Determine whether each element is in a specified set of values. |
apply() |
Apply a function to each element of the Series. |
map() |
Map values of the Series to a new set of values. |
copy() |
Create a copy of the Series. |
hasnans() |
Return a Boolean value indicating whether the Series contains any missing values. |
DataFrame Attributes
shape |
returns a tuple representing the dimensions of the DataFrame |
index |
returns the row index labels of the DataFrame. |
columns |
returns the column index labels of the DataFrame. |
values |
returns a NumPy array containing the data of the DataFrame. |
dtypes |
returns a Series containing the data types of each column in the DataFrame. |
size |
returns the total number of elements in the DataFrame. |
ndim |
returns the number of dimensions of the DataFrame. |
empty |
returns a Boolean value indicating whether the DataFrame is empty or not. |
axes |
returns a list containing the row and column axis labels of the DataFrame. |
DataFrame Indexing and selecting data
head() |
returns the first n rows of a DataFrame |
tail() |
returns the last n rows of a DataFrame |
sample() |
returns a random sample of a DataFrame |
loc[] |
label-based indexing for selecting rows and columns |
iloc[] |
integer-based indexing for selecting rows and columns |
Descriptive statistics:
info() |
prints information about a DataFrame, including data types and non-null values |
describe() |
provides summary statistics for numerical columns |
value_counts() |
counts the unique values in a column |
unique() |
returns an array of unique values in a column |
nunique() |
returns the number of unique values in a column |
rank() |
returns the rank of each value in a column |
corr() |
calculates the correlation between columns |
nlargest() |
returns the n largest values in a column |
nsmallest() |
returns the n smallest values in a column |
max() |
returns the maximum value in a column |
min() |
returns the minimum value in a column |
mean() |
returns the mean of values in a column |
var() |
returns the variance of values in a column |
Data manipulation:
rename() |
renames columns or index labels of a DataFrame |
.astype() |
converts the data type of a column |
sort_values() |
sorts a DataFrame by values |
set_index() |
sets a column as the DataFrame's index |
reset_index() |
resets the DataFrame's index to a default range index |
sort_index() |
sorts the DataFrame by index values |
drop() |
removes rows or columns from a DataFrame |
apply() |
applies a function to each element of a DataFrame |
isin() |
checks whether each element of a DataFrame is contained in a list of values |
Missing data
isnull() |
returns a boolean DataFrame indicating missing values |
notnull() |
returns a boolean DataFrame indicating non-missing values |
dropna() |
removes rows or columns containing missing values |
fillna() |
fills missing values with a specified value or method |
duplicated() |
returns a boolean Series indicating duplicate rows |
drop_duplicates() |
removes duplicate rows |
Other
size() |
returns the number of elements in a DataFrame |
insert() |
inserts a column into a DataFrame at a specified location |
copy() |
creates a copy of a DataFrame |
|
|
|
