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Pandas,让Python像R一样处理数据,但快

2017-06-25 陈同 生信宝典

What is pandas

Pandas是python中用于处理矩阵样数据的功能强大的包,提供了R中的dataframevector的操作,使得我们在使用python时,也可以方便、简单、快捷、高效地进行矩阵数据处理。

具体介绍详见http://pandas.pydata.org/。

  • A fast and efficient DataFrame object for data manipulation with integrated indexing;

  • Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;

  • Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;

  • Flexible reshaping and pivoting of data sets;

  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;

  • Columns can be inserted and deleted from data structures for size mutability;

  • Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;

  • High performance merging and joining of data sets;

  • Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;

  • Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;

  • Highly optimized for performance, with critical code paths written in Cython or C.

  • Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

%matplotlib inline

#import plotly
#plotly.offline.init_notebook_mode()

import matplotlib
matplotlib.style.use('ggplot')
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
from glob import glob

Pandas读取文件

获取目标文件

dir_1 = "pandas_data/"
glob(dir_1+'*')
['pandas_data/ENCFF289HGQ.tsv',
'pandas_data/gencode.v24.ENS2SYN',
'pandas_data/ENCFF262OBL.tsv',
'pandas_data/Gene_metadata_primary_wt_whole_cell.tsv',
'pandas_data/ENCFF673KYR.tsv',
'pandas_data/ENCFF060LPA.tsv']

查看目标文件内容和格式

Ipython中可以通过在Linux命令前加!调用系统命令,更多使用见http://ipython.org/ipython-doc/3/interactive/reference.html#system-shell-access.

!head -n 4 pandas_data/gencode.v24.ENS2SYNgene_id    gene_symbol

ENSG00000223972.5    DDX11L1

ENSG00000227232.5    WASH7P

ENSG00000278267.1    MIR6859-1
!head -n 4 pandas_data/ENCFF556YSD.tsvtranscript_id    gene_id    length    effective_length    expected_count    TPM    FPKM

ENST00000373020.4    ENSG00000000003.10    2206    1925.57    997.23    1.71    7.21

ENST00000494424.1    ENSG00000000003.10    820    539.58    24.77    0.15    0.64

ENST00000496771.1    ENSG00000000003.10    1025    744.57    0.00    0.00    0.00

读取两列文件

ens2syn_file = "pandas_data/gencode.v24.ENS2SYN"# pandas中的计数都是从0开始的
# header=0: 指定第一行包含列的名字
# index_col=0: 指定第一列为行的名字
ens2syn = pd.read_table(ens2syn_file, header=0, index_col=0)

数据表的索引

  • 数值索引和布尔值索引是按行选取

  • 字符串索引是按列选取

  • 行和列是等效的,应用于行的选取函数也可应用于列,反之亦然

按行选取数据
ens2syn[:3]

gene_symbol
gene_id
ENSG00000223972.5DDX11L1
ENSG00000227232.5WASH7P
ENSG00000278267.1MIR6859-1
取出索引中包含特定值的行
ens2syn[ens2syn.index=="ENSG00000227232.5"]

gene_symbol
gene_id
ENSG00000227232.5WASH7P
取出某列包含特定值列表的行
ens2syn[ens2syn['gene_symbol'].isin(['DDX11L1','MIR6859-1'])]

gene_symbol
gene_id
ENSG00000223972.5DDX11L1
ENSG00000278267.1MIR6859-1
使用正则表达式选取符合要求的行
# head: 只展示部分数据
ens2syn[ens2syn.index.str.contains(r'ENSG0000022')].head()

gene_symbol
gene_id
ENSG00000223972.5DDX11L1
ENSG00000227232.5WASH7P
ENSG00000222623.1RNU6-1100P
ENSG00000228463.9AP006222.2
ENSG00000224813.3SEPT14P13

读取多列文件

gzip, bzip压缩的文件也可以直接读取,但是需要保证文件后缀的正确。read_table默认参数可以自动检测文件的格式,根据文件的后缀 ‘.gz’, ‘.bz2’, ‘.zip’, or ‘xz’分别使用 gzip, bz2, zip or xz读取。

tsvL = glob(dir_1+'ENC*.tsv')
tsvL
['pandas_data/ENCFF289HGQ.tsv',
'pandas_data/ENCFF262OBL.tsv',
'pandas_data/ENCFF673KYR.tsv',
'pandas_data/ENCFF060LPA.tsv']
index = 0
tsvFile = tsvL[index]
expr = pd.read_table(tsvFile, header=0, index_col=0)
expr.head(3)

transcript_id(s)lengtheffective_lengthexpected_countTPMFPKM
gene_id





ENSG00000000003.14ENST00000373020.8,ENST00000494424.1,ENST000004…2198.691939.242827.01.0310.84
ENSG00000000005.5ENST00000373031.4,ENST00000485971.1940.50681.070.00.000.00
ENSG00000000419.12ENST00000371582.8,ENST00000371584.8,ENST000003…1079.84820.381680.01.4515.23

选取多列数据

列的输出顺序与给定的列名字的顺序一致

expr[['FPKM','TPM']].head(3)

FPKMTPM
gene_id

ENSG00000000003.1410.841.03
ENSG00000000005.50.000.00
ENSG00000000419.1215.231.45

重命名列名字

从Dataframe中只选取一列时,数据框会被转换成Series,因此需要使用pd.loc[:,[column_name]](虽然内部的方括号内只有一个值,但写法是必须的)索引。

# 因为要把多个文件的同一类型表达值合并到一个文件,我们使用文件名作为列的名字
name = os.path.split(tsvFile)[-1][:-4]
print name
expr_tpm = expr.loc[:,['TPM']] # 取出所有的行和名字为TPM的列
expr_tpm.columns=[name]  
expr_tpm[:3]
ENCFF289HGQ

ENCFF289HGQ
gene_id
ENSG00000000003.141.03
ENSG00000000005.50.00
ENSG00000000419.121.45

合并矩阵

定义函数简化文件读取
# 为了读取多个文件,定义一个函数简化操作
def readExpr_1(tsvFileL, typeL=['TPM','FPKM']):
   '''
   tsvFileL: lists of files waiting for reading
   resultD: a dictionary to save data matrix
           {'TPM':[mat1, mat2,...]
            'FPKM':[mat1, mat2, ...]}
   typeL; list of names for columns to be extracted
   '''

   resultD = {}
   for _type in typeL: resultD[_type] = []

   for tsvFile in tsvFileL:
       expr = pd.read_table(tsvFile, header=0, index_col=0)
       name = os.path.split(tsvFile)[-1][:-4]  #this options is very arbitary
       for _type in typeL: # add _ to type to avoid override Python inner function `type`
           expr_type = expr.loc[:,[_type]]
           expr_type.columns = [name]
           resultD[_type].append(expr_type)
   return resultD
#-----------------------------------------------------
exprD = readExpr_1(tsvL)
TPM_mat = exprD['TPM']
FPKM_mat = exprD['FPKM']
使用pd.merge合并矩阵示例

先从刚才读取的矩阵中选出2个测试下pandas中的矩阵合并方法和效果

# 选取第一个矩阵
_idL = ['ENSG00000000003.14', 'ENSG00000000005.5','ENSG00000000419.12','ENSG00000000457.13']
mat1 = TPM_mat[0]
mat1 = mat1[mat1.index.isin(_idL)]
mat1

ENCFF289HGQ
gene_id
ENSG00000000003.141.03
ENSG00000000005.50.00
ENSG00000000419.121.45
ENSG00000000457.130.24
# 选取第二个矩阵
_idL = ['ENSG00000001561.6','ENSG00000000003.14', 'ENSG00000000419.12','ENSG00000001036.13']
mat2 = TPM_mat[1]
mat2 = mat2[mat2.index.isin(_idL)]
mat2

ENCFF262OBL
gene_id
ENSG00000000003.1417.13
ENSG00000000419.1218.86
ENSG00000001036.1310.34
ENSG00000001561.62.47

基于索引(index)的合并

* outer: 合并所有的索引,缺失值填充NA * inner:保留共有的索引 * left:使用第一个矩阵的索引 * right:使用第二个矩阵的索引pd.merge(mat1, mat2, left_index=True, right_index=True, how="outer")

ENCFF289HGQENCFF262OBL
gene_id

ENSG00000000003.141.0317.13
ENSG00000000005.50.00NaN
ENSG00000000419.121.4518.86
ENSG00000000457.130.24NaN
ENSG00000001036.13NaN10.34
ENSG00000001561.6NaN2.47
pd.merge(mat1, mat2, left_index=True, right_index=True, how="inner")

ENCFF289HGQENCFF262OBL
gene_id

ENSG00000000003.141.0317.13
ENSG00000000419.121.4518.86
pd.merge(mat1, mat2, left_index=True, right_index=True, how="left")

ENCFF289HGQENCFF262OBL
gene_id

ENSG00000000003.141.0317.13
ENSG00000000005.50.00NaN
ENSG00000000419.121.4518.86
ENSG00000000457.130.24NaN
使用pd.concat合并矩阵示例

对于较多的数据表合并操作时,concatmerge要简单快速很多。

pd.concat([mat1, mat2], axis=1)

ENCFF289HGQENCFF262OBL
ENSG00000000003.141.0317.13
ENSG00000000005.50.00NaN
ENSG00000000419.121.4518.86
ENSG00000000457.130.24NaN
ENSG00000001036.13NaN10.34
ENSG00000001561.6NaN2.47
pd.concat([mat1, mat2], axis=1, join="inner")

ENCFF289HGQENCFF262OBL
gene_id

ENSG00000000003.141.0317.13
ENSG00000000419.121.4518.86
使用pd.join合并矩阵示例
mat3 = mat1.join(mat2, how="outer")
mat3

ENCFF289HGQENCFF262OBL
gene_id

ENSG00000000003.141.0317.13
ENSG00000000005.50.00NaN
ENSG00000000419.121.4518.86
ENSG00000000457.130.24NaN
ENSG00000001036.13NaN10.34
ENSG00000001561.6NaN2.47

替换NA值为0

mat3 = mat3.fillna(0)
mat3

ENCFF289HGQENCFF262OBL
gene_id

ENSG00000000003.141.0317.13
ENSG00000000005.50.000.00
ENSG00000000419.121.4518.86
ENSG00000000457.130.240.00
ENSG00000001036.130.0010.34
ENSG00000001561.60.002.47

去除所有值都为0的行

#Both works well here
#mat3[(mat3>0).any(axis=1)]
mat3.loc[(mat3>0).any(axis=1)]

ENCFF289HGQENCFF262OBL
gene_id

ENSG00000000003.141.0317.13
ENSG00000000419.121.4518.86
ENSG00000000457.130.240.00
ENSG00000001036.130.0010.34
ENSG00000001561.60.002.47
测试三种方法使用的内存和速度比较

速度:concat>join>>merge

内存:相当

%timeit test_merge = reduce(lambda left,right: pd.merge(left,right,left_index=True,right_index=True,how='outer'), TPM_mat)100 loops, best of 3: 3.36 ms per loop%timeit test_merge = pd.concat(TPM_mat, axis=1)1000 loops, best of 3: 1.21 ms per loop%timeit TPM_mat[0].join(TPM_mat[1:], how="outer")1000 loops, best of 3: 1.25 ms per loop%load_ext memory_profiler%memit test_merge = reduce(lambda left,right: pd.merge(left,right,left_index=True,right_index=True,how='outer'), TPM_mat)peak memory: 107.32 MiB, increment: 0.01 MiB%memit test_merge = pd.concat(TPM_mat, axis=1)peak memory: 107.30 MiB, increment: 0.10 MiB%memit TPM_mat[0].join(TPM_mat[1:], how="outer")peak memory: 107.32 MiB, increment: 0.00 MiB
重写函数完成文件的读写和矩阵的合并
# 读取多个文件,并且合并矩阵,定义一个函数简化操作
def concatExpr(tsvFileL, typeL=['TPM','FPKM']):
   '''
   tsvFileL: lists of files waiting for reading
   resultD: a dictionary to save data matrix
           {'TPM':[mat1, mat2,...]
            'FPKM':[mat1, mat2, ...]}
   typeL; list of names for columns to be extracted
   '''

   resultD = {}
   for _type in typeL: resultD[_type] = []

   for tsvFile in tsvFileL:
       expr = pd.read_table(tsvFile, header=0, index_col=0)
       name = os.path.split(tsvFile)[-1][:-4]  #this options is very arbitary
       for _type in typeL: # add _ to type to avoid override Python inner function `type`
           expr_type = expr.loc[:,[_type]]
           expr_type.columns = [name]
           resultD[_type].append(expr_type)
   #-------------------------------------------
   mergeD = {}
   for _type in typeL:
       mergeM = pd.concat(resultD[_type], axis=1)
       mergeM = mergeM.fillna(0) # Substitute all NA with 0
       mergeM = mergeM.loc[(mergeM>0).any(axis=1)] # Delete aoo zero rows.
       mergeD[_type] = mergeM
   return mergeD
#-----------------------------------------------------
typeL = ['TPM','FPKM']
exprD = concatExpr(tsvL, typeL)
TPM_mat = exprD['TPM']
FPKM_mat = exprD['FPKM']
TPM_mat.head()

ENCFF289HGQENCFF262OBLENCFF673KYRENCFF060LPA
gene_id



ENSG00000000003.141.0317.132.426.64
ENSG00000000419.121.4518.861.809.91
ENSG00000000457.130.242.480.380.86
ENSG00000000460.160.265.360.161.51
ENSG00000000938.120.000.050.000.01

矩阵数据提取

只保留表达矩阵中存储的基因的IDSymbol对照表

# 回顾下数据格式
ens2syn.head(3)

gene_symbol
gene_id
ENSG00000223972.5DDX11L1
ENSG00000227232.5WASH7P
ENSG00000278267.1MIR6859-1
ens2syn.shape(60725, 1)ens2syn = ens2syn[ens2syn.index.isin(TPM_mat.index)]ens2syn.shape(48, 1)ens2syn.head(3)

gene_symbol
gene_id
ENSG00000001460.17STPG1
ENSG00000001461.16NIPAL3
ENSG00000000938.12FGR

读取META data文件

meta = "pandas_data/meta.tsv"
metaM = pd.read_table(meta, header=0, index_col=0)
# 重名了列的名字
oriColnames = metaM.columns.values
nameD = dict([(i,i.replace(' ','_')) for i in oriColnames])
metaM.rename(columns=nameD, inplace=True)
metaM.head(3)

File formatOutput typeExperiment accessionAssayBiosample term idBiosample term nameBiosample typeBiosample life stageBiosample sexBiosample organismmd5sumFile download URLAssemblyPlatformControlled byFile StatusAudit WARNINGAudit INTERNAL_ACTIONAudit NOT_COMPLIANTAudit ERROR
File accession




















ENCFF120PLKtsvgene quantificationsENCSR198TKARNA-seqCL:0000650mesangial cellprimary cellunknown, fetalunknown, femaleHomo sapiens1e9a3db25f5361b2ca454d1df427f430https://www.encodeproject.org/files/ENCFF120PL…hg19NaNNaNreleasedNaNNaNNaNNaN
ENCFF805BVEtsvgene quantificationsENCSR198TKARNA-seqCL:0000650mesangial cellprimary cellunknown, fetalunknown, femaleHomo sapiensee0e94d6795ed7c2ef69c61b1d29eb02https://www.encodeproject.org/files/ENCFF805BV…hg19NaNNaNreleasedNaNNaNNaNNaN
ENCFF850RHDtsvgene quantificationsENCSR198TKARNA-seqCL:0000650mesangial cellprimary cellunknown, fetalunknown, femaleHomo sapiens22f948135c0935516f19f6b995ccc30chttps://www.encodeproject.org/files/ENCFF850RH…GRCh38NaNNaNreleasedNaNNaNNaNNaN

3 rows × 47 columns

只保留前面提到的4个样品的数据
sampleL = TPM_mat.columns.values
metaM = metaM[metaM.index.isin(sampleL)]
# 同时索引行和列
metaM.ix[:4,:5]

Biosample term idBiosample term nameBiosample typeBiosample life stageBiosample sex
File accession




ENCFF673KYRCL:0000650mesangial cellprimary cellunknown, fetalunknown, female
ENCFF262OBLCL:1001568pulmonary artery endothelial cellprimary celladultmale
ENCFF060LPACL:1001568pulmonary artery endothelial cellprimary celladultmale
ENCFF289HGQCL:0002558fibroblast of villous mesenchymeprimary cellnewbornmale, female
提取目标列信息
# 假如只提取`Biosample`开头的列
#meta_colL = ['Biosample term id', 'Biosample term name']

# Extract columns matching specific patterns
# Both works well, filter is more simple
#metaM.loc[:,metaM.columns.str.contains(r'^Biosample')]
metaM = metaM.filter(regex=("^Biosample"))
metaM

Biosample term idBiosample term nameBiosample typeBiosample life stageBiosample sexBiosample organismBiosample treatmentsBiosample subcellular fraction term nameBiosample phaseBiosample synchronization stageBiosample Age
File accession










ENCFF673KYRCL:0000650mesangial cellprimary cellunknown, fetalunknown, femaleHomo sapiensNaNNaNNaNNaNNaN
ENCFF262OBLCL:1001568pulmonary artery endothelial cellprimary celladultmaleHomo sapiensNaNNaNNaNNaN52 year
ENCFF060LPACL:1001568pulmonary artery endothelial cellprimary celladultmaleHomo sapiensNaNNaNNaNNaN23 year
ENCFF289HGQCL:0002558fibroblast of villous mesenchymeprimary cellnewbornmale, femaleHomo sapiensNaNNaNNaNNaNNaN
metaM.fillna('')

Biosample term idBiosample term nameBiosample typeBiosample life stageBiosample sexBiosample organismBiosample treatmentsBiosample subcellular fraction term nameBiosample phaseBiosample synchronization stageBiosample Age
File accession










ENCFF673KYRCL:0000650mesangial cellprimary cellunknown, fetalunknown, femaleHomo sapiens




ENCFF262OBLCL:1001568pulmonary artery endothelial cellprimary celladultmaleHomo sapiens



52 year
ENCFF060LPACL:1001568pulmonary artery endothelial cellprimary celladultmaleHomo sapiens



23 year
ENCFF289HGQCL:0002558fibroblast of villous mesenchymeprimary cellnewbornmale, femaleHomo sapiens




Pandas写入文件

写入文本文件

metaM.to_csv("pandas_data/meta2.tsv", sep="\t")ens2syn.to_csv("pandas_data/gencode.v24.ENS2SYN", sep="\t")TPM_mat.to_csv("pandas_data/TPM", sep=b'\t', float_format="%.2f")

写入HDF5文件

HDF5 is a data model, library, and file format for storing and managing data. It supports an unlimited variety of datatypes, and is designed for flexible and efficient I/O and for high volume and complex data. HDF5 is portable and is extensible, allowing applications to evolve in their use of HDF5. The HDF5 Technology suite includes tools and applications for managing, manipulating, viewing, and analyzing data in the HDF5 format. https://support.hdfgroup.org/HDF5/

使用优势是把处理好的数据以二进制文件存取,既可以减少文件数目、压缩使用空间,又可以方便多次快速读取,并且可以在不同的程序语言如Python与R中共同使用。

HDF5文件的写入形式上类似于字典操作,其读取也是。

# 写入模式打开一个HDF5文件,使用压缩格式以节省空间
store = pd.HDFStore("pandas_data/ENCODE.hdf5", "w", complib=str("zlib"), complevel=9)

# 写入表达矩阵
store["TPM"] = TPM_mat
store["FPKM"] = FPKM_mat

# 写入注释文件
store['ens2syn'] = ens2syn
store['meta'] = metaM

# 关闭HDF5句柄
store.close()
/MPATHB/soft/anacond/lib/python2.7/site-packages/IPython/core/interactiveshell.py:3035: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block0_values] [items->['Biosample term id', 'Biosample term name', 'Biosample type', 'Biosample life stage', 'Biosample sex', 'Biosample organism', 'Biosample Age']]

 exec(code_obj, self.user_global_ns, self.user_ns)

当数据中存在混合数据模式时,会出现上面的Warning,对于我们的数据只要把metaM中的NaN值替换掉就可以。

# 写入模式打开一个HDF5文件,使用压缩格式已节省空间
store = pd.HDFStore("pandas_data/ENCODE.hdf5", "w", complib=str("zlib"), complevel=9)

# 写入表达矩阵
store["TPM"] = TPM_mat
store["FPKM"] = FPKM_mat

# 写入注释文件
store['ens2syn'] = ens2syn
store['meta'] = metaM.fillna('')

# 关闭HDF5句柄
store.close()

读取HDF5文件

store = pd.HDFStore("pandas_data/ENCODE.hdf5")# 列出HDF5文件的索引名字
store.keys()
['/FPKM', '/TPM', '/ens2syn', '/meta']TPM_mat = store['TPM']
TPM_mat.head(3)

ENCFF289HGQENCFF262OBLENCFF673KYRENCFF060LPA
gene_id



ENSG00000000003.141.0317.132.426.64
ENSG00000000419.121.4518.861.809.91
ENSG00000000457.130.242.480.380.86
ens2syn = store['ens2syn']
meta = store['meta']
store.close()

PANDAS矩阵的小应用

利用上面的矩阵操作,选取这两个基因相关的信息并绘制表达谱

targetL = ['KRIT1','AK2']

Gene_symbol转换为Gene_id

ensID = ens2syn[ens2syn["gene_symbol"].isin(targetL)]
ensID

gene_symbol
gene_id
ENSG00000004455.16AK2
ENSG00000001631.14KRIT1

提取目标基因的表达

targetExpr = TPM_mat[TPM_mat.index.isin(ensID.index)] targetExpr

ENCFF289HGQENCFF262OBLENCFF673KYRENCFF060LPA
gene_id



ENSG00000001631.141.1513.361.376.21
ENSG00000004455.162.3137.628.9515.57

重命名矩阵的索引

ensID_dict = ensID.to_dict() ensID_dict{'gene_symbol': {'ENSG00000001631.14': 'KRIT1', 'ENSG00000004455.16': 'AK2'}}targetExpr = targetExpr.rename(index=ensID_dict['gene_symbol'])
targetExpr

ENCFF289HGQENCFF262OBLENCFF673KYRENCFF060LPA
gene_id



KRIT11.1513.361.376.21
AK22.3137.628.9515.57

转置矩阵以增加META信息

targetExpr_t = targetExpr.T targetExpr_t
gene_idKRIT1AK2
ENCFF289HGQ1.152.31
ENCFF262OBL13.3637.62
ENCFF673KYR1.378.95
ENCFF060LPA6.2115.57

从meta矩阵中提取4列信息

meta_type = ["Biosample term name","Biosample type", "Biosample life stage", "Biosample sex"]meta = meta[meta_type] meta

Biosample term nameBiosample typeBiosample life stageBiosample sex
File accession



ENCFF673KYRmesangial cellprimary cellunknown, fetalunknown, female
ENCFF262OBLpulmonary artery endothelial cellprimary celladultmale
ENCFF060LPApulmonary artery endothelial cellprimary celladultmale
ENCFF289HGQfibroblast of villous mesenchymeprimary cellnewbornmale, female

修改下矩阵信息,去除unknow,字符串(只是为了展示方便)

meta.loc['ENCFF673KYR',"Biosample life stage"] = "fetal"
# Much faster
meta = meta.set_value('ENCFF673KYR','Biosample sex','female')
meta = meta.set_value('ENCFF289HGQ','Biosample sex','female')
meta

Biosample term nameBiosample typeBiosample life stageBiosample sex
File accession



ENCFF673KYRmesangial cellprimary cellfetalfemale
ENCFF262OBLpulmonary artery endothelial cellprimary celladultmale
ENCFF060LPApulmonary artery endothelial cellprimary celladultmale
ENCFF289HGQfibroblast of villous mesenchymeprimary cellnewbornfemale
target_expr_meta = targetExpr_t.join(meta, how="left")
target_expr_meta
gene_idKRIT1AK2Biosample term nameBiosample typeBiosample life stageBiosample sex
ENCFF289HGQ1.152.31fibroblast of villous mesenchymeprimary cellnewbornfemale
ENCFF262OBL13.3637.62pulmonary artery endothelial cellprimary celladultmale
ENCFF673KYR1.378.95mesangial cellprimary cellfetalfemale
ENCFF060LPA6.2115.57pulmonary artery endothelial cellprimary celladultmale

重名了列名字(替换掉名字中的空格)

oriColnames = target_expr_meta.columns.values
nameD = dict([(i,i.replace(' ','_')) for i in oriColnames])
target_expr_meta.rename(columns=nameD, inplace=True)
target_expr_meta
gene_idKRIT1AK2Biosample_term_nameBiosample_typeBiosample_life_stageBiosample_sex
ENCFF289HGQ1.152.31fibroblast of villous mesenchymeprimary cellnewbornfemale
ENCFF262OBL13.3637.62pulmonary artery endothelial cellprimary celladultmale
ENCFF673KYR1.378.95mesangial cellprimary cellfetalfemale
ENCFF060LPA6.2115.57pulmonary artery endothelial cellprimary celladultmale

绘制散点图

target_expr_meta.plot.scatter(x='KRIT1', y='AK2')<matplotlib.axes._subplots.AxesSubplot at 0x7fbcaefc0c10>/MPATHB/soft/anacond/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning:

elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison


绘制箱线图

a = target_expr_meta.boxplot(["KRIT1", "AK2"])/MPATHB/soft/anacond/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: FutureWarning:

The default value for 'return_type' will change to 'axes' in a future release.
To use the future behavior now, set return_type='axes'.
To keep the previous behavior and silence this warning, set return_type='dict'.


使用Plotly绘制交互图

fig = {
   'data': [
       {
           'x': target_expr_meta.KRIT1,
           'y': target_expr_meta.AK2,
           'text': target_expr_meta.Biosample_term_name,
           'mode': 'markers+texts',
           'name': 'Legend name',
           'marker': {
               'symbol':"circle",
               "opacity": "0.9"
           }
       },
       {
           'x': [0,40],
           'y': [0,40],
           'text': "Diagonal",
           'mode': 'lines',
           'name': 'Diagonal line',
           'showlegend': False,
           'line': {
               'color': ('rgb(192,192,192)')
           }
       }
   ],
   'layout': {
       'xaxis': {'title':'KRIT1 expression value','range':[0,40]},
       'yaxis': {'title':'AK2 expression value','range':[0,40]},
       'height':500,
       'width':600,
       'showlegend': True,
        "legend": {
           "x": 0.6,
           "y": 1
       }
   }
}
plotly.offline.iplot(fig)

python fig = {    'data': [        {            'x': target_expr_meta[target_expr_meta['Biosample_sex']==atype].KRIT1,            'y': target_expr_meta[target_expr_meta['Biosample_sex']==atype].AK2,            'text': target_expr_meta[target_expr_meta['Biosample_sex']==atype].Biosample_term_name,            'mode': 'markers+texts',            'name': _type,            'marker': {                'symbol':"circle",                "opacity": "0.9"            }        } for atype in ['female','male']    ],    'layout': {        'xaxis': {'title':'KRIT1 expression value','range':[0,40]},        'yaxis': {'title':'AK2 expression value','range':[0,40]},        'height':500,        'width':600,        'showlegend': True,         "legend": {            "x": 0.6,            "y": 1        }    } } plotly.offline.iplot(fig)### 使用R读取HDF5文件r #R code for reading hdf5 > h5ls('test.hdf5')  group          name       otype  dclass      dim 0         /          FPKM   H5I_GROUP                 1     /FPKM         axis0 H5I_DATASET  STRING        3 2     /FPKM         axis1 H5I_DATASET  STRING    25135 3     /FPKM  block0_items H5I_DATASET  STRING        3 4     /FPKM block0_values H5I_DATASET   FLOAT  x 25135 5         /           TPM   H5I_GROUP                 6      /TPM         axis0 H5I_DATASET  STRING        3 7      /TPM         axis1 H5I_DATASET  STRING    24025 8      /TPM  block0_items H5I_DATASET  STRING        3 9      /TPM block0_values H5I_DATASET   FLOAT  x 24025 10        /       ens2syn   H5I_GROUP                 11 /ens2syn         axis0 H5I_DATASET  STRING        1 12 /ens2syn         axis1 H5I_DATASET  STRING    60725 13 /ens2syn  block0_items H5I_DATASET  STRING        1 14 /ens2syn block0_values H5I_DATASET    VLEN        1 15        /          meta   H5I_GROUP                 16    /meta         axis0 H5I_DATASET  STRING       47 17    /meta         axis1 H5I_DATASET  STRING        3 18    /meta  block0_items H5I_DATASET  STRING       19 19    /meta block0_values H5I_DATASET   FLOAT      x 3 20    /meta  block1_items H5I_DATASET  STRING        2 21    /meta block1_values H5I_DATASET INTEGER      x 3 22    /meta  block2_items H5I_DATASET  STRING       26 23    /meta block2_values H5I_DATASET    VLEN        1 > TPM = h5read("test.hdf5", "/TPM") > str(TPM) List of 4 $ axis0        : chr [1:3(1d)] "ENCFF673KYR" "ENCFF805ZGF" "ENCFF581ZEU" $ axis1        : chr [1:24025(1d)] "ENSG00000000003.14" "ENSG00000000005.5" "ENSG00000000419.12" "ENSG00000000457.13" ... $ block0_items : chr [1:3(1d)] "ENCFF673KYR" "ENCFF805ZGF" "ENCFF581ZEU" $ block0_values: num [1:3, 1:24025] 2.42 1.64 5.69 0 0 0.11 1.8 3.82 6.38 0.38 ... > d <- TPM$block0_values > rownames(d) <- TPM$axis1 Error in `rownames<-`(`*tmp*`, value = c("ENSG00000000003.14", "ENSG00000000005.5",  :  length of 'dimnames' [1] not equal to array extent > d <- as.data.frame(TPM$block0_values) > rownames(d) <- TPM$axis1 Error in `row.names<-.data.frame`(`*tmp*`, value = value) :  invalid 'row.names' length > dims(d) Error: could not find function "dims" > dim(d) [1]     3 24025 > d <- t(as.data.frame(TPM$block0_values)) > dim(d) [1] 24025     3 > rownames(d) <- TPM$axis1 > colnames(d) <- TPM$axis0 > hed(d) Error: could not find function "hed" > head(d)               ENCFF673KYR ENCFF805ZGF ENCFF581ZEU ENSG00000000003.14        2.42        1.64        5.69 ENSG00000000005.5         0.00        0.00        0.11 ENSG00000000419.12        1.80        3.82        6.38 ENSG00000000457.13        0.38        0.57        1.17 ENSG00000000460.16        0.16        0.31        0.14 ENSG00000000938.12        0.00        0.03        0.00### Pandas矩阵生成python np.random.seed(1) df = pd.DataFrame({"first": np.random.rand(100),                   "second": np.random.rand(100),                   "class": np.random.randint(0, 2, (100,))},                   index=range(100)) df.head()


classfirstsecond
000.4170220.326645
100.7203240.527058
210.0001140.885942
310.3023330.357270
410.1467560.908535

Ipython notebook link

https://github.com/Tong-Chen/notebook/blob/master/pandas.ipynb

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