How do I create pandas dataframe based on two conditions on different columns?

Question

I want to create control_df dataframe based on the following conditions: (i) "mrna_assignment" column contains substring "control //" (ii) "probeset_type" column contains substring "control"

import pandas as pd
import numpy as np

df.columns = df.iloc[0]
df = df[1:].set_index("Gene Symbol") # set Gene Symbol as row index
df.sort_index() # Sort by row index
df

## Samples are "control" if:
## (i) "mrna_assignment" column contains substring "control //"; OR
## (ii) "probeset_type" column contains substring "control"
control_df = df[df["mrna_assignment"].str.contains("control //")] or df[df["category"].str.contains("control->")]
control_df

Traceback:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-167-516e7c765379> in <module>()
      3 ## (i) "mrna_assignment" column contains substring "control //"; OR
      4 ## (ii) "probeset_type" column contains substring "control"
----> 5 control_df = df[df["mrna_assignment"].str.contains("control //")] or df[df["category"].str.contains("control->")]
      6 control_df

/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in __nonzero__(self)
   1536     def __nonzero__(self):
   1537         raise ValueError(
-> 1538             f"The truth value of a {type(self).__name__} is ambiguous. "
   1539             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1540         )

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Sample data:

df.iloc[0:50,0:50].to_dict()

{'Cytoband': {'---': '---'},
 'Entrez Gene': {'---': '---'},
 'GO Biological Process': {'---': nan},
 'GO Cellular Component': {'---': nan},
 'GO Molecular Function': {'---': nan},
 'GO_biological_process': {'---': '---'},
 'GO_cellular_component': {'---': '---'},
 'GO_molecular_function': {'---': '---'},
 'Gene Title': {'---': '---'},
 'Pathway': {'---': nan},
 'Probe ID': {'---': '7892552'},
 'Protein Domains': {'---': nan},
 'Swiss-Prot': {'---': '---'},
 'UniGene': {'---': nan},
 'category': {'---': 'normgene->intron'},
 'crosshyb_type': {'---': '---'},
 'gene_assignment': {'---': '---'},
 'mrna_assignment': {'---': '--- // --- // neg_control // --- // --- // --- // --- // --- // ---'},
 nan: {'---': nan},
 nan: {'---': 6.89},
 nan: {'---': 5.64},
 nan: {'---': 6.31},
 nan: {'---': 6.24},
 nan: {'---': 6.17},
 nan: {'---': 5.8},
 nan: {'---': 5.94},
 nan: {'---': 5.95},
 nan: {'---': 5.66},
 nan: {'---': 6.38},
 nan: {'---': 4.84},
 nan: {'---': 5.78},
 nan: {'---': 6.5},
 nan: {'---': 7.21},
 nan: {'---': 6.03},
 nan: {'---': 6.74},
 nan: {'---': 6.3},
 nan: {'---': 6.05},
 nan: {'---': 6.35},
 nan: {'---': 6.74},
 nan: {'---': 6.6},
 nan: {'---': 6.84},
 'pathway': {'---': '---'},
 'protein_domains': {'---': '---'},
 'seqname': {'---': '---'},
 'start': {'---': '---'},
 'stop': {'---': '---'},
 'strand': {'---': '---'},
 'swissprot': {'---': '---'},
 'total_probes': {'---': '4'},
 'unigene': {'---': '---'}}