Data Exploration and Analysis

  1. Descriptive statistics: Pandas provides functions to compute common summary statistics like mean, median, mode, standard deviation, etc.
  2. Filtering and slicing: You can filter data based on specific criteria or select subsets of data using various methods.
  3. Aggregation and grouping: Pandas allows grouping data based on one or more variables and performing aggregations on them.
  4. Merging and joining: You can combine multiple datasets based on common columns or indices.
  5. Time series analysis: Pandas has excellent support for working with time series data, including date/time indexing and resampling.

append() Adding rows list , dictionary to DataFrame
Settings Managing DataFrame display option values
apply Applying function along an Axis or elements
atGet and Set data using rows and columns
astypeCast to a specified dtype
cutUsing segments for categorizing values
describeDescriptive statistics of DataFrame or series
groupbycombining data and aggregate functions
get_dummiesConvert categorical variable into dummy/indicator variables
headFirst n rows of the DataFrame
isnullChecking NaN or None data
ilocValues at different position using integer
infoDetail information about the Dataframe
locValues at different position using column label
filterCondition based filtering of rows
fillnaFilling NaN data
mergecombining DataFrame and aggregate functions
methodsPandas DataFrame methods
nlargestn elements in descending sorted values
maskconditional replacement of data
queryFiltering data by using conditions
countNumber of rows or columns with different options
maxMax value of required axis
meanMean value of required axis
minMin value of required axis
rollingrolling window calculations
shape, size, ndimDimensions of the DataFrame
stdStandard Deviation on required axis
sumSum of values of required axis
set_indexCreating index using one or more columns
sort_valuesSort columns in ascending or descending
tolistFrom DataFrame to List
notnullNot None and Not Null values checking
pivot()Reshaped data based on column values
pivot_table()Reshaped data with aggregate functions based on column values
reset_indexRemove index of the DataFrame
value_countscounts of unique values
uniqueUnique Data of a column
whereData updating based on condition

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