import pandas as pd import numpy as np # ============ delete / fill Null value ============ #
in many instances , If you use Pandas To read a lot of data , It is often found that there are incomplete parts in the original data . # stay DataFrame Missing data location in , Pandas
Will automatically fill in a null value , such as NaN or Null . # We can choose to use it .dropna() To discard these auto populated values ; # Or use .fillna()
Automatically fill these null values with data . # ------- delete -------- # 1, Instance data source ( With null value ) dt_01 = {'A':[1, np.nan, 3],
'B':[2,np.nan,np.nan], 'C':[4,5,6]} my_datafarme_01 = pd.DataFrame(dt_01)
#print(my_datafarme_01) # 2, When you use .dropna() Method , It's telling Pandas Delete rows with one or more null values ( Or column ).
# Delete line with .dropna(axis=0) , # Delete column with .dropna(axis=1) . # Please note that , If you don't specify axis
parameter , The default is to delete rows . #print(my_datafarme_01.dropna())
#print(my_datafarme_01.dropna(axis=0)) #print(my_datafarme_01.dropna(axis=1)) #
--------- Fill all Nan-------- # allied , If you use .fillna() method ,Pandas Will be on this DataFrame
Fill in the default value you specified for all the null values in . # such as , Set all NaN replace with 20 : #print(my_datafarme_01.fillna('20'))
# --------- Fills the Nan----------- # It's too big to avoid , So we can choose to do this only for certain rows or columns Nan value fill col
= ['A','B'] my_datafarme_01[col] = my_datafarme_01[col].fillna('10')
#print(my_datafarme_01) # In the same way ,.dropna() and .fillna() It doesn't change your data permanently , Unless you pass in
inplace=True parameter . # =============== Group statistics =============== # Pandas
Group statistics function of : You can group data rows by the contents of a column , Statistical function is applied to it , Such as summation , average , median , Standard deviation and so on … # example : We can use the
.groupby() method , Press 'Company' Column to group , Combined use .mean() Average each group : # ------- data sheet ------- dt_01 =
{'Company':['GOOGLE','GOOGLE','ORACLE','ORACLE','TWITIER','TWITIER'],
'Person':['Saa','Charlie','Amy','Vanessa','Carl','Sarah'], 'Sales':[200, 120,
340, 124, 243, 350] } my_datafarme_02 = pd.DataFrame(dt_01)
#print(my_datafarme_02) # -------- Group and average -------- # then , call .groupby() method , And continue to use
.mean() Average : #print(my_datafarme_02.groupby('Company').mean()) # use .count()
method , Yes DataFrame Count the number of times an element in the .
#print(my_datafarme_02.groupby('Company').count()) #
================== data description ============== # Pandas Of .describe() Method will be applied to DataFrame
Inside << data >> Analysis ,( Only data element columns will be analyzed ) # And generate a number of descriptive statistical indicators at one time , Convenient for users to have an intuitive understanding of the data . #
Generated metrics , From left to right : count , average , standard deviation , minimum value ,(25% 50% 75%) Quantile , Maximum .
#print(my_datafarme_02.groupby('Company').describe()) #
--------- Convert display styles ( Vertical row )-----------
#print(my_datafarme_02.groupby('Company').describe().transpose()) #
--------- Specifies the data description for the index -------
#print(my_datafarme_02.groupby('Company').describe().transpose()['GOOGLE']) #
================== Stacking (Concat)============= # Stacking : Basically, it's a simple way to put multiple DataFrame
Pile them together , Put together a bigger one DataFrame. # When you stack , Be sure to pay attention to the index of your data table and the extension direction of columns , Stack in the same direction . #
1,--- data source ---- #dt_02 =
pd.DataFrame(np.array(['A0','B0','C0','D0','A1','B1','C1','D1','A2','B2','C2','D2','A3','B3','C3','D3']).reshape(4,4),[0,1,2,3],['A','B','C','D'])
#dt_03 =
pd.DataFrame(np.array(['A4','B4','C4','D4','A5','B5','C5','D5','A6','B6','C6','D6','A7','B7','C7','D7']).reshape(4,4),[4,5,6,7],['A','B','C','D'])
#dt_04 =
pd.DataFrame(np.array(['A8','B8','C8','D8','A9','B9','C9','D9','A10','B10','C10','D10','A11','B11','C11','D11']).reshape(4,4),[8,9,10,11],['A','B','C','D'])
#print(dt_02) #print(dt_03) #print(dt_04) # 2,--- Default stack ( Stack by row )----- # We use it
pd.concat() Stack it into a large table : #print(pd.concat([dt_02,dt_03,dt_04])) #
3,--- Specifies the stacking direction ( Stack by column )----- #print(pd.concat([dt_02,dt_03,dt_04], axis=1)) #
=================== Merger (Merge)================( Used for cases with more common listing )( Merge common columns ) # use pd.merge()
function , Can combine multiple DataFrame Merge together , It is merged in a similar way SQL Data table mode ; # The basic syntax of merge operation is : pd.merge(left,
right, how='inner', on='Key') . # among left The parameter represents the DataFrame, and right The parameter represents the
DataFrame; # how='inner' It's about the left and right DataFrame There are non coincident Key Time , How to get the results :inner
Representative intersection ;Outer Union of Representatives . # last ,on='Key' Represents the column in which the key values need to be merged , Finally, the whole table will be merged according to this column . #
------- data source -------- left_data_01 = pd.DataFrame({'KEY': ['K0','K1','K2','K3'],
'A': ['A0','A1','A2','A3'], 'B': ['B0','B1','B2','B3'] }) right_data_01 =
pd.DataFrame({'KEY': ['K0','K1','K2','K3'], 'C': ['C0','C1','C2','C3'], 'D':
['D0','D1','D2','D3'] }) # ------- Two DataFrame Data sheet merging ----- ( It is equivalent to inner join query in database )
#print(pd.merge(left_data_01,right_data_01,how='inner',on='KEY')) # -------- Multiple
on parameter ------- left_data_02 = pd.DataFrame({'KEY1': ['K0','K0','K1','K2'],
'KEY2': ['K0','K1','K0','K1'], 'A': ['A0','A1','A2','A3'], 'B':
['B0','B1','B2','B3'] }) #print(left_data_02) right_data_02 =
pd.DataFrame({'KEY1': ['K0','K1','K1','K2'], 'KEY2': ['K0','K0','K0','K0'],
'C': ['C0','C1','C2','C3'], 'D': ['D0','D1','D2','D3'] }) #print(right_data_02)
#print(pd.merge(left_data_02,right_data_02, on='KEY1'))
#print(pd.merge(left_data_02,right_data_02, on='KEY2'))
#print(pd.merge(left_data_02,right_data_02, on=['KEY1','KEY2'])) #
================== connect (join)=================( Used in situations where there is not much common listing )( Merge common keys ) # and .merge()
Different , The join takes the index as the common key , Not a column # ------- data source ------- left_data_03 = pd.DataFrame({'A':
['A0','A1','A2'], 'B': ['B0','B1','B2']}, index=['K0','K1','K2']) right_data_03
= pd.DataFrame({'C': ['C0','C2','C3'], 'D': ['D0','D2','D3']},
index=['K0','K2','K3']) #print(left_data_03) #print(right_data_03) #
------- connect ---------( Default left connection )( Based on the index of the left table ) #print(left_data_03.join(right_data_03)) #
------- intersection ------- #print(left_data_03.join(right_data_03, how='inner')) #
------- Union ------- #print(left_data_03.join(right_data_03, how='outer')) #
================== Find non duplicate values =============== # Non repeating values , In a DataFrame
It's always unique , Distinctive . Non duplicate values found , It is helpful to avoid sample deviation in data analysis # stay Pandas in , Mainly used 3 Methods : # ----1, First of all
.unique() method . Like the one below DataFrame in , lookup col2 All non duplicate values in the column : df = pd.DataFrame({'col1':
[1,2,3,4], 'col2': [444,555,666,444], 'col3': ['abc','def','ghi','xyz']})
#print(df) #print(df['col2'].unique()) # ================= Find the number of distinct values ===========
# ----2, In addition to listing all non duplicate values , We can still use it .nunique() method , Gets the number of all non duplicate values :
#print(df['col2'].nunique()) # ================= Find unique values and their number =========== #
----3, in addition , It works .value_counts() Get a count of all values and corresponding values at the same time : #print(df['col2'].value_counts())
# ================== Custom processing function =============== # use .apply() method , Yes, yes DataFrame
User defined function for data in , Data processing . # example : such as , Let's define a square() function , Then check the col1 Column applies this function : def
square(x): return x*x #print(df['col1'].apply(square)) # ---- Use built-in functions -----
#print(df['col3'].apply(len)) # ---- use lambda Expressions define functions ------
#print(df['col1'].apply(lambda x:x*x)) # ================== obtain DataFrame
Properties of =========== # DataFrame Properties of : include column and Indexes The name of #print(df.columns)
#print(df.index) # ================== sort ================= #
If you want to sort the entire table by the value of a column , It can be used .sort_values() : # It should be noted that , Table index index
Or corresponding to the row before sorting , The original index data is not lost because of sorting . #print(df.sort_values('col2')) #
================ Find null values =============== # If you have a large data set , You can use it Pandas Of .isnull()
method , Finding null values in a table easily and quickly : # This returns a new one DataFrame, Boolean values are used in it (True/False) Original DataFrame
Is the data at the corresponding position in is null . #print(df.isnull()) # ================ PivotTable =============== #
PivotTable : It's a summary table , It shows the summary statistical results of the data in the original table . # Pandas
The PivotTable can automatically group data for you , section , screen , sort , count , Sum or average , And the results are displayed intuitively . # ---- data source -------- data_02 =
{ 'A':['Dog', 'Dog', 'Dog', 'Goat', 'Goat', 'Goat'], 'B':['Brown', 'Brown',
'Black', 'Black', 'Brown', 'Brown'], 'C':['x', 'y', 'x', 'y', 'x', 'y'],
'D':[1,3,2,5,4,1] } df_02 = pd.DataFrame(data_02) print(df_02) # ----- PivotTable ----
# Pandas The syntax of a PivotTable is .pivot_table(data, values='', index=[''], columns=['']) , #
among values Represents the column where the data points we need to summarize statistics are located , # index Indicates that the index is grouped by the column , # and columns
It means that the final results will be broken down by the data in the column . #print(pd.pivot_table(df_02, values='D', index=['A','B'],
columns=['C'])) # Above is through pandas Call this method , It can also be done through DataFrame Object calls this method directly :
#print(df_02.pivot_table(values='D', index=['A','B'], columns=['C']))
 

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