Python学习路径：从入门到精通
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Data Exploration
using Pandas
CHEATSHEET
1. Reading and Writing Data
a. Reading a CSV file
>>>df=pd.read_csv(‘AnalyticsVidhya.csv’)
b. Writing content of data frame to CSV file
>>>df.to_csv(‘AV.csv’)
c. Reading an Excel file
>>>df=pd.read_excel(‘AV.xlsx’,‘sheet1’)
d. Writing content of data frame to Excel file
>>>df.to_excel(‘AV2.xlsx’,sheet_name=’sheet2’)
2.Getting Preview of Dataframe
a. Looking at top n records
>>>df.head(5)
b. Looking at bottom n records
>>>df.tail(5)
c. View columns name
>>>df.columns
3. Rename Columns of Data Frame
a. Rename method helps to rename column of data frame.
>>>df2=df.rename(columns={‘old_columnname’:’new_columnname’})
This statement will create a new data frame with new column name.
b. To rename the column of existing data frame, set inplace=True.
>>>df.rename(columns={‘old_columnname’:’new_columnname’}, inplace=True)
4. Selecting Columns or Rows
a. Accessing sub data frames
>>>df[[‘column1’,’column2’]]
b. Filtering Records
>>>df[ df[‘column1’]>10]
>>>df[ (df[‘column1’]>10) & df[‘column2’]==30]
>>>df[ (df[‘column1’]>10) | df[‘column2’]==30]
5. Handling Missing Values
This is an inevitale part of dealing with data . To overcome this hurdle, use
dropna or fillna function.
a. dropna: It is used to drop rows or columns having missing data
b. fillna: It is used to fill missing values
>>>df2.fillna(value=5) #It replaces all missing values with 5
>>>mean=df2[‘column1’].mean()
>>>df2[‘column1’].fillna(mean) #It replaces all missing values of column1 with mean
of available values
>>>df1.dropna()
6. Creating New Columns
New column is a function of existing columns
>>>df[‘NewColumn1’]=df[‘column2’] #Create a copy of existing column2
>>>df[‘NewColumn2’]=df[‘column2’]+10 #Add 10 to existing column2 then create a new one
>>>df[‘NewColumn3’]= df[‘column1’] + df[‘column2’] #Add elements of column1 and column2
then create new column
Aggregate
a. Groupby: Groupby helps to perform three operations
i. Splitting the data into groups
ii. Applying a function to each group individually
iii. Combining the result into a data structure
b. Pivot Table: It helps to generate data structure. It has three components
index, columns and values (similar to excel)
>>>pd.pivot_table(df, values=’column1’, index=[‘column2’,’column3’], columns=[‘column4’])
By default, it shows the sum of values column but you can change it using
argument aggfunc
>>>pd.pivot_table(df, values=’column1’, index=[‘column2’,’column3’], columns=[‘column4’], aggfunc=len)
#it shows count
7. Aggregate
>>>df.groupby(‘column1’).sum()
>>>df.groupby([‘column1’,’column2’]).count()
c. Cross Tab: Cross Tab computes the simple cross tabulation of two factors.
>>>pd.crosstab(df.column1, df.column2)
8. Merging/ Concatenating DataFrames
It performs similar operation like we do in SQL.
a. Concatenating: It concatenate two or more data frames based on their columns.
>>>pd.concat([df1,df2])
b. Merging: We can perform left, right and inner join also.
>>>pd.merge(df1, df2, on=’column1’, how=’inner’)
>>>pd.merge(df1, df2, on=’column1’, how=’left’)
>>>pd.merge(df1, df2, on=’column1’, how=’right’)
>>>pd.merge(df1, df2, on=’column1’, how=’outer’)
9. Applying function to element, column or dataframe
a. Map: It iterates over each element of a series.
>>>df[‘column1’].map(lambda x: 10+x #this will add 10 to each element of column1
>>>df[‘column2’].map(lambda x: ‘AV’+x) #this will concatenate “AV“ at the beginning of
each element of column2 (column format is string)
b. Apply: As the name suggests, applies a function along any axis of the
DataFrame.
>>>df[[‘column1’,’column2’]].apply(sum) #it will returns the sum of all the values of
column1 and column2.
c. ApplyMap: This helps to apply a function to each element of dataframe.
>>>func = lambda x: x+2
>>>df.applymap(func) #it will add 2 to each element of dataframe (all columns of
dataframe must be numeric type)
10. Identify unique values
Function unique helps to return unique values of a column.
>>>df[‘Column1’].unique()
11. Basic Stats
Pandas helps to understand the data using basic statistical methods.
a. describe: This returns the quick stats (count, mean, std, min, first quartile,
median, third quartile, max) on suitable columns
>>>df.describe()
b. covariance: It returns the co-variance between suitable columns.
>>>df.cov()
c. correlation: It returns the co-variance between suitable columns.
>>>df.corr()
To learn more, we recommend
Wes Mckinney’s Python for Data Analysis
Book for Learning Pandas
For more resources on analytics / data science, visit
www.analyticsvidhya.com
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