“MUST KNOW” from PythonPandas for Data Science.
Pandas is very popular Python library for data analysis, manipulation, and visualization, I would like to share my personal view on the list of most often used functions/snippets for data analysis.
1.Import Pandas to Python
import pandas as pd
2. Import data from CSV/Excel file
df=pd.read_csv('C:/Folder/mlhype.csv') #imports whole csv to pd dataframe df = pd.read_csv('C:/Folder/mlhype.csv', usecols=['abv', 'ibu']) #imports selected columns df = pd.read_excel('C:/Folder/mlhype.xlsx') #imports excel file
3. Save data to CSV/Excel
df.to_csv('C:/Folder/mlhype.csv') #saves data frame to csv df.to_excel('C:/Folder/mlhype.xlsx') #saves data frame to excel
4. Exploring data
df.head(5) #returns top 5 rows of data df.tail(5) #returns bottom 5 rows of data df.sample(5) #returns random 5 rows of data df.shape #returns number of rows and columns df.info() #returns index,data types, memory information df.describe() #returns basic statistical summary of columns
5. Basic statistical functions
df.mean() #returns mean of columns df.corr() #returns correlation table df.count() #returns count of nonnull's in column df.max() #returns max value in each column df.min() #returns min value in each column df.median() #returns median of each colun df.std() #returns standard deviation of each column
6. Selecting subsets
df['ColumnName'] #returns column 'ColumnName' df[['ColumnName1','ColumnName2']] #returns multiple columns from the list df.iloc[2,:] #selection by position  whole second row df.iloc[:,2] #selection by position  whole second column df.loc[:10,'ColumnName'] #returns first 11 rows of column df.ix[2,'ColumnName'] #returns second element of column
7. Data cleansing
df.columns = ['a','b','c','d','e','f','g','h'] #rename column names df.dropna() #drops all rows that contain missing values df.fillna(0) #replaces missing values with 0 (or any other passed value) df.fillna(df.mean()) #replaces missing values with mean(or any other passed function)
8.Filtering/sorting
df[df['ColumnName'] > 0.08] #returns rows with meeting criterion df[(df['ColumnName1']>2004) & (df['ColumnName2']==9)] #returns rows meeting multiple criteria df.sort_values('ColumnName') #sorts by column in ascending order df.sort_values('ColumnName',ascending=False) #sort by column in descending order
9. Data frames concatenation
pd.concat([DateFrame1, DataFrame2],axis=0) #concatenate rows vertically pd.concat([DateFrame1, DataFrame2],axis=1) #concatenate rows horizontally
10.Adding new columns
df['NewColumn'] = 50 #creates new column with value 50 in each row df['NewColumn3'] = df['NewColumn1']+df['NewColumn2'] #new column with value equal to sum of other columns del df['NewColumn'] #deletes column
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